Separating Data

db_blocos <- db %>% 
  select("rh_age_read_br", "rh_read_b4_yng_br", "rh_read_since_yng_br", "rh_read_b4_old_br", 
    "rh_read_with_b4_old_br", "rh_readin_b4_old_br", "rh_read_since_old_br", 
    "rh_read_w_since_old_br", "rh_readin_since_old_br", "rh_read_time_b4_br",
    "rh_read_time_since_br",
    "hlr_books_b4_corrected", 
    "hlr_dbooks_b4_corrected",
    "hlr_games_b4_corrected",
    "hlr_dgames_b4_corrected",
    "hlr_ad_books_b4_corrected",
    "hlr_ad_dbooks_b4_corrected",
    "hlr_ad_news_b4_corrected",
    "hlr_books_since_corrected",
    "hlr_dbooks_since_corrected",
    "hlr_games_since_corrected",
    "hlr_dgames_since_corrected",
    "hlr_ad_books_since_corrected",
    "hlr_ad_dbooks_since_corrected",
    "hlr_ad_news_since_corrected",
    "ea_read_b4_ALL_br",
    "ea_indr_b4_ALL_br",
    "ea_letter_b4_yng_br",
    "ea_tv_b4_yng_br",
    "ea_boardg_b4_yng_br",
    "ea_vidg_b4_yng_br",
    "ea_edapp_b4_yng_br",
    "ea_read_since_yng_br",
    "ea_indr_since_yng_br",
    "ea_letter_since_yng_br",
    "ea_tv_since_yng_br",
    "ea_boardg_since_yng_br",
    "ea_vidg_since_yng_br",
    "ea_edapp_since_yng_br")

db_out_na <- db_blocos[rowSums(is.na(db_blocos)) < 1, ]

db_bloco_3 <- db_out_na %>% 
  dplyr::select(
    "ea_read_b4_ALL_br",
    "ea_indr_b4_ALL_br",
    "ea_letter_b4_yng_br",
    "ea_tv_b4_yng_br",
    "ea_boardg_b4_yng_br",
    "ea_vidg_b4_yng_br",
    "ea_edapp_b4_yng_br",
    "ea_read_since_yng_br",
    "ea_indr_since_yng_br",
    "ea_letter_since_yng_br",
    "ea_tv_since_yng_br",
    "ea_boardg_since_yng_br",
    "ea_vidg_since_yng_br",
    "ea_edapp_since_yng_br"
  )

db_bloco_3_pre <- db_bloco_3 %>% 
  select(
    "ea_read_b4_ALL_br",
    "ea_indr_b4_ALL_br",
    "ea_letter_b4_yng_br",
    "ea_tv_b4_yng_br",
    "ea_boardg_b4_yng_br",
    "ea_vidg_b4_yng_br",
    "ea_edapp_b4_yng_br"
  )

db_bloco_3_pos <- db_bloco_3 %>% 
  select(
    "ea_read_since_yng_br",
    "ea_indr_since_yng_br",
    "ea_letter_since_yng_br",
    "ea_tv_since_yng_br",
    "ea_boardg_since_yng_br",
    "ea_vidg_since_yng_br",
    "ea_edapp_since_yng_br"
  )

Analise Fatorial

Matriz de correlação entre as variáveis do bloco 3

matcorr_3 <- cor(db_bloco_3)
print(matcorr_3, digits = 2)
##                        ea_read_b4_ALL_br ea_indr_b4_ALL_br ea_letter_b4_yng_br
## ea_read_b4_ALL_br                   1.00              0.40                0.39
## ea_indr_b4_ALL_br                   0.40              1.00                0.39
## ea_letter_b4_yng_br                 0.39              0.39                1.00
## ea_tv_b4_yng_br                     0.31              0.37                0.48
## ea_boardg_b4_yng_br                 0.30              0.43                0.61
## ea_vidg_b4_yng_br                   0.22              0.32                0.39
## ea_edapp_b4_yng_br                  0.21              0.23                0.37
## ea_read_since_yng_br                0.72              0.31                0.42
## ea_indr_since_yng_br                0.38              0.71                0.36
## ea_letter_since_yng_br              0.33              0.32                0.73
## ea_tv_since_yng_br                  0.30              0.31                0.39
## ea_boardg_since_yng_br              0.26              0.33                0.57
## ea_vidg_since_yng_br                0.23              0.30                0.30
## ea_edapp_since_yng_br               0.21              0.23                0.29
##                        ea_tv_b4_yng_br ea_boardg_b4_yng_br ea_vidg_b4_yng_br
## ea_read_b4_ALL_br                 0.31                0.30              0.22
## ea_indr_b4_ALL_br                 0.37                0.43              0.32
## ea_letter_b4_yng_br               0.48                0.61              0.39
## ea_tv_b4_yng_br                   1.00                0.42              0.68
## ea_boardg_b4_yng_br               0.42                1.00              0.42
## ea_vidg_b4_yng_br                 0.68                0.42              1.00
## ea_edapp_b4_yng_br                0.61                0.34              0.66
## ea_read_since_yng_br              0.31                0.32              0.24
## ea_indr_since_yng_br              0.29                0.34              0.21
## ea_letter_since_yng_br            0.42                0.54              0.35
## ea_tv_since_yng_br                0.66                0.37              0.54
## ea_boardg_since_yng_br            0.34                0.62              0.33
## ea_vidg_since_yng_br              0.51                0.29              0.61
## ea_edapp_since_yng_br             0.47                0.32              0.47
##                        ea_edapp_b4_yng_br ea_read_since_yng_br
## ea_read_b4_ALL_br                    0.21                 0.72
## ea_indr_b4_ALL_br                    0.23                 0.31
## ea_letter_b4_yng_br                  0.37                 0.42
## ea_tv_b4_yng_br                      0.61                 0.31
## ea_boardg_b4_yng_br                  0.34                 0.32
## ea_vidg_b4_yng_br                    0.66                 0.24
## ea_edapp_b4_yng_br                   1.00                 0.25
## ea_read_since_yng_br                 0.25                 1.00
## ea_indr_since_yng_br                 0.18                 0.49
## ea_letter_since_yng_br               0.33                 0.48
## ea_tv_since_yng_br                   0.49                 0.40
## ea_boardg_since_yng_br               0.29                 0.39
## ea_vidg_since_yng_br                 0.49                 0.29
## ea_edapp_since_yng_br                0.68                 0.31
##                        ea_indr_since_yng_br ea_letter_since_yng_br
## ea_read_b4_ALL_br                      0.38                   0.33
## ea_indr_b4_ALL_br                      0.71                   0.32
## ea_letter_b4_yng_br                    0.36                   0.73
## ea_tv_b4_yng_br                        0.29                   0.42
## ea_boardg_b4_yng_br                    0.34                   0.54
## ea_vidg_b4_yng_br                      0.21                   0.35
## ea_edapp_b4_yng_br                     0.18                   0.33
## ea_read_since_yng_br                   0.49                   0.48
## ea_indr_since_yng_br                   1.00                   0.44
## ea_letter_since_yng_br                 0.44                   1.00
## ea_tv_since_yng_br                     0.34                   0.49
## ea_boardg_since_yng_br                 0.45                   0.68
## ea_vidg_since_yng_br                   0.30                   0.35
## ea_edapp_since_yng_br                  0.28                   0.37
##                        ea_tv_since_yng_br ea_boardg_since_yng_br
## ea_read_b4_ALL_br                    0.30                   0.26
## ea_indr_b4_ALL_br                    0.31                   0.33
## ea_letter_b4_yng_br                  0.39                   0.57
## ea_tv_b4_yng_br                      0.66                   0.34
## ea_boardg_b4_yng_br                  0.37                   0.62
## ea_vidg_b4_yng_br                    0.54                   0.33
## ea_edapp_b4_yng_br                   0.49                   0.29
## ea_read_since_yng_br                 0.40                   0.39
## ea_indr_since_yng_br                 0.34                   0.45
## ea_letter_since_yng_br               0.49                   0.68
## ea_tv_since_yng_br                   1.00                   0.46
## ea_boardg_since_yng_br               0.46                   1.00
## ea_vidg_since_yng_br                 0.70                   0.38
## ea_edapp_since_yng_br                0.62                   0.40
##                        ea_vidg_since_yng_br ea_edapp_since_yng_br
## ea_read_b4_ALL_br                      0.23                  0.21
## ea_indr_b4_ALL_br                      0.30                  0.23
## ea_letter_b4_yng_br                    0.30                  0.29
## ea_tv_b4_yng_br                        0.51                  0.47
## ea_boardg_b4_yng_br                    0.29                  0.32
## ea_vidg_b4_yng_br                      0.61                  0.47
## ea_edapp_b4_yng_br                     0.49                  0.68
## ea_read_since_yng_br                   0.29                  0.31
## ea_indr_since_yng_br                   0.30                  0.28
## ea_letter_since_yng_br                 0.35                  0.37
## ea_tv_since_yng_br                     0.70                  0.62
## ea_boardg_since_yng_br                 0.38                  0.40
## ea_vidg_since_yng_br                   1.00                  0.67
## ea_edapp_since_yng_br                  0.67                  1.00

Mapa de calor para a correlação entre as variáveis do bloco 3

Pode-se observar que as variáveis que apresentam maior correlação são justamente aquelas variáveis pré e pós pandemais, tal com ea_read_b4_AAL_br e ea_read_since_yng_br que indicam a frequeência com a que a criança se envolvia em alguma atividade de leitura.

corrplot(matcorr_3, method="circle")

Matriz de correlação entre as variáveis do bloco 3 pré-pandemia

matcorr_pre_3 <- cor(db_bloco_3_pre)
print(matcorr_pre_3, digits = 2)
##                     ea_read_b4_ALL_br ea_indr_b4_ALL_br ea_letter_b4_yng_br
## ea_read_b4_ALL_br                1.00              0.40                0.39
## ea_indr_b4_ALL_br                0.40              1.00                0.39
## ea_letter_b4_yng_br              0.39              0.39                1.00
## ea_tv_b4_yng_br                  0.31              0.37                0.48
## ea_boardg_b4_yng_br              0.30              0.43                0.61
## ea_vidg_b4_yng_br                0.22              0.32                0.39
## ea_edapp_b4_yng_br               0.21              0.23                0.37
##                     ea_tv_b4_yng_br ea_boardg_b4_yng_br ea_vidg_b4_yng_br
## ea_read_b4_ALL_br              0.31                0.30              0.22
## ea_indr_b4_ALL_br              0.37                0.43              0.32
## ea_letter_b4_yng_br            0.48                0.61              0.39
## ea_tv_b4_yng_br                1.00                0.42              0.68
## ea_boardg_b4_yng_br            0.42                1.00              0.42
## ea_vidg_b4_yng_br              0.68                0.42              1.00
## ea_edapp_b4_yng_br             0.61                0.34              0.66
##                     ea_edapp_b4_yng_br
## ea_read_b4_ALL_br                 0.21
## ea_indr_b4_ALL_br                 0.23
## ea_letter_b4_yng_br               0.37
## ea_tv_b4_yng_br                   0.61
## ea_boardg_b4_yng_br               0.34
## ea_vidg_b4_yng_br                 0.66
## ea_edapp_b4_yng_br                1.00

Mapa de calor para a correlação entre as variáveis do bloco 3 pré-pandemia

Pode-se observar que as variávies ea_read_b4_AAL_br e ea_edapp_b4_yng_br possuem a menor correlação, ou seja, crianças que se envolvem em alguma atividade de leitura e crianças que utilizam algum aplicativo educacional em tablet são pouco correlacionadas, enquanto que as variáveis ea_vidg_b4_yng_br e ea_read_b4_AAL_br têm a maior correlação, ou seja, crianças que se envolvem em alguma atividade de leitura são fortemente correlacionadas com crianças que assitem vídeos ou jogam jogos educacionais no computador.

corrplot(matcorr_pre_3, method="circle")

Matriz de correlação entre as variáveis do bloco 3 pós-pandemia

matcorr_pos_3 <- cor(db_bloco_3_pos)
print(matcorr_pos_3, digits = 2)
##                        ea_read_since_yng_br ea_indr_since_yng_br
## ea_read_since_yng_br                   1.00                 0.49
## ea_indr_since_yng_br                   0.49                 1.00
## ea_letter_since_yng_br                 0.48                 0.44
## ea_tv_since_yng_br                     0.40                 0.34
## ea_boardg_since_yng_br                 0.39                 0.45
## ea_vidg_since_yng_br                   0.29                 0.30
## ea_edapp_since_yng_br                  0.31                 0.28
##                        ea_letter_since_yng_br ea_tv_since_yng_br
## ea_read_since_yng_br                     0.48               0.40
## ea_indr_since_yng_br                     0.44               0.34
## ea_letter_since_yng_br                   1.00               0.49
## ea_tv_since_yng_br                       0.49               1.00
## ea_boardg_since_yng_br                   0.68               0.46
## ea_vidg_since_yng_br                     0.35               0.70
## ea_edapp_since_yng_br                    0.37               0.62
##                        ea_boardg_since_yng_br ea_vidg_since_yng_br
## ea_read_since_yng_br                     0.39                 0.29
## ea_indr_since_yng_br                     0.45                 0.30
## ea_letter_since_yng_br                   0.68                 0.35
## ea_tv_since_yng_br                       0.46                 0.70
## ea_boardg_since_yng_br                   1.00                 0.38
## ea_vidg_since_yng_br                     0.38                 1.00
## ea_edapp_since_yng_br                    0.40                 0.67
##                        ea_edapp_since_yng_br
## ea_read_since_yng_br                    0.31
## ea_indr_since_yng_br                    0.28
## ea_letter_since_yng_br                  0.37
## ea_tv_since_yng_br                      0.62
## ea_boardg_since_yng_br                  0.40
## ea_vidg_since_yng_br                    0.67
## ea_edapp_since_yng_br                   1.00

Mapa de calor para a correlação entre as variáveis do bloco 3 pós-pandemia

Pode-se observar que a menor correlação continua sendo entre as variáveis ea_read_b4_AAL_br e ea_edapp_b4_yng_br, enquanto que a maior correlação passar a ser entre as variáveis ea_tv_since_yng_br e ea_vidg_since_yng_br, ou seja, entre crianças que assistem a vídeos/programas educacionais com computador e na TV.

corrplot(matcorr_pos_3, method="circle")

Multiple Correspondence Analysis

PRÉ-PANDEMIA

db_bloco_3_pref <- as.data.frame(lapply(db_bloco_3_pre, as.factor))
summary(db_bloco_3_pref)
##  ea_read_b4_ALL_br ea_indr_b4_ALL_br ea_letter_b4_yng_br ea_tv_b4_yng_br
##  0:113             0:114             0:198               0:121          
##  1:181             1:163             1:188               1:137          
##  2:106             2:115             2:113               2:126          
##  3: 51             3: 64             3: 36               3: 55          
##  4:114             4:109             4: 57               4:111          
##  5: 39             5: 34             5: 18               5: 42          
##  6: 11             6: 16             6:  5               6: 23          
##  ea_boardg_b4_yng_br ea_vidg_b4_yng_br ea_edapp_b4_yng_br
##  0:158               0:115             0:156             
##  1:208               1:150             1:132             
##  2:135               2:127             2:110             
##  3: 55               3: 60             3: 69             
##  4: 32               4: 97             4: 87             
##  5: 16               5: 39             5: 32             
##  6: 11               6: 27             6: 29

Podemos observar nos gráficos abaixo a frequência absoluta respostas para cada categoria de cada pergunta.

for (i in 1:7) {
  plot(db_bloco_3_pref[,i], main=colnames(db_bloco_3_pref)[i],
       ylab = "Count", col="steelblue", las = 2)
  }

MCA com pacote FactoMineR

MCA(db_bloco_3_pref, ncp = 5, graph = TRUE) 
## Warning: ggrepel: 27 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 7 variables
## *The results are available in the following objects:
## 
##    name              description                       
## 1  "$eig"            "eigenvalues"                     
## 2  "$var"            "results for the variables"       
## 3  "$var$coord"      "coord. of the categories"        
## 4  "$var$cos2"       "cos2 for the categories"         
## 5  "$var$contrib"    "contributions of the categories" 
## 6  "$var$v.test"     "v-test for the categories"       
## 7  "$ind"            "results for the individuals"     
## 8  "$ind$coord"      "coord. for the individuals"      
## 9  "$ind$cos2"       "cos2 for the individuals"        
## 10 "$ind$contrib"    "contributions of the individuals"
## 11 "$call"           "intermediate results"            
## 12 "$call$marge.col" "weights of columns"              
## 13 "$call$marge.li"  "weights of rows"
res.mca_pref_3 <- MCA(db_bloco_3_pref, graph = FALSE)
print(res.mca_pref_3)
## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 7 variables
## *The results are available in the following objects:
## 
##    name              description                       
## 1  "$eig"            "eigenvalues"                     
## 2  "$var"            "results for the variables"       
## 3  "$var$coord"      "coord. of the categories"        
## 4  "$var$cos2"       "cos2 for the categories"         
## 5  "$var$contrib"    "contributions of the categories" 
## 6  "$var$v.test"     "v-test for the categories"       
## 7  "$ind"            "results for the individuals"     
## 8  "$ind$coord"      "coord. for the individuals"      
## 9  "$ind$cos2"       "cos2 for the individuals"        
## 10 "$ind$contrib"    "contributions of the individuals"
## 11 "$call"           "intermediate results"            
## 12 "$call$marge.col" "weights of columns"              
## 13 "$call$marge.li"  "weights of rows"

Visualização e interpretação

Pode-se observar que 51% da variância é explicada pelas primeiras 10 dimensões

eig.val_pref_3 <- get_eigenvalue(res.mca_pref_3)
eig.val_pref_3
##        eigenvalue variance.percent cumulative.variance.percent
## Dim.1  0.53965386        8.9942310                    8.994231
## Dim.2  0.43300683        7.2167804                   16.211011
## Dim.3  0.38417548        6.4029247                   22.613936
## Dim.4  0.36257435        6.0429058                   28.656842
## Dim.5  0.33287255        5.5478759                   34.204718
## Dim.6  0.25225400        4.2042334                   38.408951
## Dim.7  0.22067876        3.6779793                   42.086930
## Dim.8  0.18603825        3.1006375                   45.187568
## Dim.9  0.17535371        2.9225618                   48.110130
## Dim.10 0.17415158        2.9025264                   51.012656
## Dim.11 0.16450366        2.7417277                   53.754384
## Dim.12 0.15453608        2.5756013                   56.329985
## Dim.13 0.14931659        2.4886098                   58.818595
## Dim.14 0.14184741        2.3641235                   61.182718
## Dim.15 0.13603103        2.2671838                   63.449902
## Dim.16 0.13033998        2.1723330                   65.622235
## Dim.17 0.12630414        2.1050690                   67.727304
## Dim.18 0.12051967        2.0086612                   69.735965
## Dim.19 0.11845507        1.9742511                   71.710216
## Dim.20 0.11530086        1.9216811                   73.631898
## Dim.21 0.11212570        1.8687616                   75.500659
## Dim.22 0.10839202        1.8065336                   77.307193
## Dim.23 0.10647306        1.7745510                   79.081744
## Dim.24 0.09834111        1.6390185                   80.720762
## Dim.25 0.09721966        1.6203276                   82.341090
## Dim.26 0.09502890        1.5838151                   83.924905
## Dim.27 0.09108665        1.5181108                   85.443016
## Dim.28 0.08801978        1.4669963                   86.910012
## Dim.29 0.08201853        1.3669755                   88.276988
## Dim.30 0.07843515        1.3072525                   89.584240
## Dim.31 0.07216892        1.2028153                   90.787055
## Dim.32 0.07053652        1.1756086                   91.962664
## Dim.33 0.06937332        1.1562220                   93.118886
## Dim.34 0.06211855        1.0353092                   94.154195
## Dim.35 0.06102216        1.0170360                   95.171231
## Dim.36 0.05183080        0.8638467                   96.035078
## Dim.37 0.05091034        0.8485056                   96.883584
## Dim.38 0.04960845        0.8268075                   97.710391
## Dim.39 0.04085766        0.6809611                   98.391352
## Dim.40 0.03505673        0.5842788                   98.975631
## Dim.41 0.03231867        0.5386445                   99.514275
## Dim.42 0.02914347        0.4857245                  100.000000

Representação gráfica da porcentagem explicada por cada dimensão

fviz_screeplot(res.mca_pref_3, addlabels = TRUE, ylim = c(0, 10))

Biplot de indivíduos e categorias de variáveis:

Os pontos azuis representam indivíduos e os pontos vermelhos representam as variáveis, quanto mais próximos uns dos outros mais similares são essas observações. Podemos observar um cluster de indivíduos que não são explicados pela dimensão 1 e nem pela dimensãso 2 no canto inferior esquerdo do gráfico.

fviz_mca_biplot(res.mca_pref_3, 
               repel = TRUE, # Avoid text overlapping (slow if many point)
               ggtheme = theme_minimal())
## Warning: ggrepel: 532 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 26 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

#Gráfico de variáveis #Resultados Usada para extrair os resultados para categorias de variáveis. Esta função retorna uma lista contendo as coordenadas, o cos2 e a contribuição das categorias de variáveis:

var_pref_3 <- get_mca_var(res.mca_pref_3)
var_pref_3
## Multiple Correspondence Analysis Results for variables
##  ===================================================
##   Name       Description                  
## 1 "$coord"   "Coordinates for categories" 
## 2 "$cos2"    "Cos2 for categories"        
## 3 "$contrib" "contributions of categories"
# Coordinates
var_pref_3$coord
##                              Dim 1      Dim 2        Dim 3        Dim 4
## ea_read_b4_ALL_br_0   -0.365576095  0.5221916  0.532895429 -0.481676016
## ea_read_b4_ALL_br_1   -0.328143613  0.2851807 -0.256338476  0.163790113
## ea_read_b4_ALL_br_2   -0.226260687 -0.4935147 -0.320572505  0.421249292
## ea_read_b4_ALL_br_3   -0.124136091 -0.4496996 -0.313451599  0.287104599
## ea_read_b4_ALL_br_4    0.369503882 -0.6549571  0.413918765 -0.348789576
## ea_read_b4_ALL_br_5    1.993024610  0.5854658 -0.579641711 -0.375317566
## ea_read_b4_ALL_br_6    1.015205808  1.4958027  1.051462295  1.808001506
## ea_indr_b4_ALL_br_0   -0.371243515  0.5747139  0.747631394 -0.617634955
## ea_indr_b4_ALL_br_1   -0.518267187  0.3385811 -0.459077438  0.261336258
## ea_indr_b4_ALL_br_2   -0.247909241 -0.4027810 -0.226148337  0.380782753
## ea_indr_b4_ALL_br_3    0.134273909 -0.6735214 -0.187389077  0.199150971
## ea_indr_b4_ALL_br_4    0.575818652 -0.6464343  0.347329537 -0.473985781
## ea_indr_b4_ALL_br_5    1.936485777  0.6666111 -0.711466136  0.023761758
## ea_indr_b4_ALL_br_6    1.131912209  1.0322277  0.870658262  1.383340404
## ea_letter_b4_yng_br_0 -0.395504573  0.3554691  0.437156725 -0.496542171
## ea_letter_b4_yng_br_1 -0.378972736  0.2532449 -0.373414405  0.196356039
## ea_letter_b4_yng_br_2 -0.004989159 -0.5855608 -0.297402447  0.626528161
## ea_letter_b4_yng_br_3  0.421848357 -1.0160964 -0.223724017  0.007325075
## ea_letter_b4_yng_br_4  1.077173243 -0.9656051  0.700464767 -0.401430691
## ea_letter_b4_yng_br_5  3.524956845  1.9487305 -1.290736852 -0.089321603
## ea_letter_b4_yng_br_6  2.017183161  0.9434529  1.722437867  2.965673572
## ea_tv_b4_yng_br_0     -0.590258403  0.6009218  0.592189473 -0.768691332
## ea_tv_b4_yng_br_1     -0.636850661  0.5379464 -0.509270133  0.093803774
## ea_tv_b4_yng_br_2     -0.309305268 -0.5316640 -0.456419843  0.545564047
## ea_tv_b4_yng_br_3      0.025139995 -0.7488060 -0.105804944  0.655548537
## ea_tv_b4_yng_br_4      0.649574395 -0.9668301  0.576081258 -0.450736210
## ea_tv_b4_yng_br_5      2.301910599  0.9268541 -1.278739396 -0.632645480
## ea_tv_b4_yng_br_6      1.194633402  1.3110461  2.226316910  2.259440180
## ea_boardg_b4_yng_br_0 -0.307128326  0.5597988  0.627588031 -0.351394252
## ea_boardg_b4_yng_br_1 -0.385695313  0.2118604 -0.389384401 -0.047585075
## ea_boardg_b4_yng_br_2 -0.103979726 -0.5189409 -0.352573620  0.460101029
## ea_boardg_b4_yng_br_3  0.584827066 -1.0221739  0.067075476 -0.047860244
## ea_boardg_b4_yng_br_4  0.918271506 -1.5009540  0.976433862 -0.353292632
## ea_boardg_b4_yng_br_5  3.589976186  1.5882341 -1.969301342 -1.164541394
## ea_boardg_b4_yng_br_6  2.163487993  1.4891075  2.364024712  3.261335314
## ea_vidg_b4_yng_br_0   -0.544985110  0.5597291  0.592744767 -0.832223747
## ea_vidg_b4_yng_br_1   -0.623846661  0.5027695 -0.515470759  0.127388271
## ea_vidg_b4_yng_br_2   -0.290110478 -0.5534331 -0.460178432  0.568482271
## ea_vidg_b4_yng_br_3    0.252129365 -0.6362411 -0.002691924  0.634073827
## ea_vidg_b4_yng_br_4    0.576966008 -1.0483415  0.642645585 -0.653294772
## ea_vidg_b4_yng_br_5    2.243485345  0.7541302 -1.486543357 -0.885057994
## ea_vidg_b4_yng_br_6    1.277960164  1.5168234  2.348063372  2.379358033
## ea_edapp_b4_yng_br_0  -0.345347260  0.4118886  0.356905068 -0.583357363
## ea_edapp_b4_yng_br_1  -0.616777602  0.4640648 -0.450047773  0.026300709
## ea_edapp_b4_yng_br_2  -0.313581062 -0.4133528 -0.591427791  0.597683813
## ea_edapp_b4_yng_br_3   0.068119640 -0.6240383  0.054334256  0.622372635
## ea_edapp_b4_yng_br_4   0.590140228 -1.2551249  0.647761519 -0.621980263
## ea_edapp_b4_yng_br_5   2.237227325  0.9097543 -1.386862499 -0.922782303
## ea_edapp_b4_yng_br_6   1.453413915  1.4862068  1.829705398  2.154635874
##                             Dim 5
## ea_read_b4_ALL_br_0    0.36875268
## ea_read_b4_ALL_br_1   -0.56629968
## ea_read_b4_ALL_br_2    0.43817218
## ea_read_b4_ALL_br_3    0.47854146
## ea_read_b4_ALL_br_4   -0.14389993
## ea_read_b4_ALL_br_5    0.28299791
## ea_read_b4_ALL_br_6   -0.42300023
## ea_indr_b4_ALL_br_0    0.47166329
## ea_indr_b4_ALL_br_1   -0.64531617
## ea_indr_b4_ALL_br_2    0.52005743
## ea_indr_b4_ALL_br_3    0.43672385
## ea_indr_b4_ALL_br_4   -0.50958478
## ea_indr_b4_ALL_br_5    0.41873650
## ea_indr_b4_ALL_br_6    0.31048064
## ea_letter_b4_yng_br_0  0.39251595
## ea_letter_b4_yng_br_1 -0.66838755
## ea_letter_b4_yng_br_2  0.61285072
## ea_letter_b4_yng_br_3  0.56876092
## ea_letter_b4_yng_br_4 -0.90791934
## ea_letter_b4_yng_br_5  0.67985157
## ea_letter_b4_yng_br_6 -0.45494971
## ea_tv_b4_yng_br_0      0.64879881
## ea_tv_b4_yng_br_1     -0.85759874
## ea_tv_b4_yng_br_2      0.58421451
## ea_tv_b4_yng_br_3      0.61751319
## ea_tv_b4_yng_br_4     -0.67736415
## ea_tv_b4_yng_br_5      0.24768148
## ea_tv_b4_yng_br_6     -0.16535147
## ea_boardg_b4_yng_br_0  0.34407342
## ea_boardg_b4_yng_br_1 -0.45201964
## ea_boardg_b4_yng_br_2  0.47634505
## ea_boardg_b4_yng_br_3 -0.02020594
## ea_boardg_b4_yng_br_4 -0.84112765
## ea_boardg_b4_yng_br_5  0.56781783
## ea_boardg_b4_yng_br_6 -0.51888807
## ea_vidg_b4_yng_br_0    0.70537334
## ea_vidg_b4_yng_br_1   -0.80675027
## ea_vidg_b4_yng_br_2    0.61161799
## ea_vidg_b4_yng_br_3    0.54415619
## ea_vidg_b4_yng_br_4   -0.76587303
## ea_vidg_b4_yng_br_5    0.29215759
## ea_vidg_b4_yng_br_6   -0.27906338
## ea_edapp_b4_yng_br_0   0.52403307
## ea_edapp_b4_yng_br_1  -0.82941540
## ea_edapp_b4_yng_br_2   0.66002090
## ea_edapp_b4_yng_br_3   0.38393279
## ea_edapp_b4_yng_br_4  -0.74894682
## ea_edapp_b4_yng_br_5   0.21629521
## ea_edapp_b4_yng_br_6  -0.45251936
var_pref_3$cos2
##                              Dim 1      Dim 2        Dim 3        Dim 4
## ea_read_b4_ALL_br_0   3.008363e-02 0.06138107 6.392323e-02 5.222576e-02
## ea_read_b4_ALL_br_1   4.490728e-02 0.03391791 2.740416e-02 1.118830e-02
## ea_read_b4_ALL_br_2   1.066120e-02 0.05072105 2.140132e-02 3.695443e-02
## ea_read_b4_ALL_br_3   1.393437e-03 0.01828673 8.884481e-03 7.453691e-03
## ea_read_b4_ALL_br_4   3.106742e-02 0.09760967 3.898498e-02 2.768179e-02
## ea_read_b4_ALL_br_5   2.689475e-01 0.02320840 2.274895e-02 9.537618e-03
## ea_read_b4_ALL_br_6   1.876999e-02 0.04074782 2.013461e-02 5.953239e-02
## ea_indr_b4_ALL_br_0   3.136064e-02 0.07515719 1.271868e-01 8.680223e-02
## ea_indr_b4_ALL_br_1   9.686271e-02 0.04134039 7.600131e-02 2.462910e-02
## ea_indr_b4_ALL_br_2   1.413557e-02 0.03731348 1.176291e-02 3.334897e-02
## ea_indr_b4_ALL_br_3   2.094169e-03 0.05269037 4.078655e-03 4.606735e-03
## ea_indr_b4_ALL_br_4   7.142454e-02 0.09001705 2.598720e-02 4.839568e-02
## ea_indr_b4_ALL_br_5   2.194479e-01 0.02600446 2.962179e-02 3.304151e-05
## ea_indr_b4_ALL_br_6   3.422305e-02 0.02846061 2.024830e-02 5.111534e-02
## ea_letter_b4_yng_br_0 7.427320e-02 0.05999745 9.074098e-02 1.170689e-01
## ea_letter_b4_yng_br_1 6.323331e-02 0.02823653 6.139205e-02 1.697534e-02
## ea_letter_b4_yng_br_2 5.603113e-06 0.07718247 1.990966e-02 8.836004e-02
## ea_letter_b4_yng_br_3 1.106462e-02 0.06419390 3.112069e-03 3.336170e-06
## ea_letter_b4_yng_br_4 1.185255e-01 0.09524446 5.012025e-02 1.646121e-02
## ea_letter_b4_yng_br_5 3.746328e-01 0.11449902 5.023120e-02 2.405532e-04
## ea_letter_b4_yng_br_6 3.335269e-02 0.00729593 2.431797e-02 7.209197e-02
## ea_tv_b4_yng_br_0     8.533806e-02 0.08844929 8.589735e-02 1.447313e-01
## ea_tv_b4_yng_br_1     1.162433e-01 0.08294127 7.433427e-02 2.521932e-03
## ea_tv_b4_yng_br_2     2.465110e-02 0.07283434 5.367731e-02 7.669255e-02
## ea_tv_b4_yng_br_3     6.207333e-05 0.05506977 1.099478e-03 4.220699e-02
## ea_tv_b4_yng_br_4     9.292878e-02 0.20586985 7.309033e-02 4.474426e-02
## ea_tv_b4_yng_br_5     3.883932e-01 0.06296764 1.198557e-01 2.933699e-02
## ea_tv_b4_yng_br_6     5.544667e-02 0.06677933 1.925662e-01 1.983389e-01
## ea_boardg_b4_yng_br_0 3.261224e-02 0.10834400 1.361727e-01 4.269040e-02
## ea_boardg_b4_yng_br_1 7.602521e-02 0.02293869 7.748650e-02 1.157205e-03
## ea_boardg_b4_yng_br_2 3.040814e-03 0.07574052 3.496167e-02 5.953864e-02
## ea_boardg_b4_yng_br_3 3.359151e-02 0.10261816 4.418778e-04 2.249699e-04
## ea_boardg_b4_yng_br_4 4.628323e-02 0.12365628 5.233197e-02 6.850947e-03
## ea_boardg_b4_yng_br_5 3.442519e-01 0.06737863 1.035899e-01 3.622455e-02
## ea_boardg_b4_yng_br_6 8.524418e-02 0.04038386 1.017794e-01 1.937076e-01
## ea_vidg_b4_yng_br_0   6.831202e-02 0.07205825 8.080966e-02 1.592972e-01
## ea_vidg_b4_yng_br_1   1.255434e-01 0.08154103 8.571294e-02 5.234765e-03
## ea_vidg_b4_yng_br_2   2.190336e-02 0.07971025 5.511076e-02 8.410421e-02
## ea_vidg_b4_yng_br_3   6.872348e-03 0.04376246 7.834007e-07 4.346482e-02
## ea_vidg_b4_yng_br_4   6.233650e-02 0.20580101 7.733659e-02 7.992090e-02
## ea_vidg_b4_yng_br_5   3.407914e-01 0.03850657 1.496226e-01 5.303781e-02
## ea_vidg_b4_yng_br_6   7.499306e-02 0.10564684 2.531664e-01 2.599597e-01
## ea_edapp_b4_yng_br_0  4.053442e-02 0.05765957 4.329297e-02 1.156595e-01
## ea_edapp_b4_yng_br_1  1.039642e-01 0.05885510 5.535337e-02 1.890435e-04
## ea_edapp_b4_yng_br_2  2.141909e-02 0.03721714 7.619119e-02 7.781159e-02
## ea_edapp_b4_yng_br_3  5.864097e-04 0.04921289 3.730817e-04 4.895053e-02
## ea_edapp_b4_yng_br_4  5.738465e-02 0.25957281 6.913781e-02 6.374389e-02
## ea_edapp_b4_yng_br_5  2.747272e-01 0.04542863 1.055719e-01 4.673906e-02
## ea_edapp_b4_yng_br_6  1.045392e-01 0.10930975 1.656772e-01 2.297461e-01
##                              Dim 5
## ea_read_b4_ALL_br_0   3.060871e-02
## ea_read_b4_ALL_br_1   1.337462e-01
## ea_read_b4_ALL_br_2   3.998321e-02
## ea_read_b4_ALL_br_3   2.070762e-02
## ea_read_b4_ALL_br_4   4.711816e-03
## ea_read_b4_ALL_br_5   5.422613e-03
## ea_read_b4_ALL_br_6   3.258644e-03
## ea_indr_b4_ALL_br_0   5.062106e-02
## ea_indr_b4_ALL_br_1   1.501738e-01
## ea_indr_b4_ALL_br_2   6.220574e-02
## ea_indr_b4_ALL_br_3   2.215349e-02
## ea_indr_b4_ALL_br_4   5.593825e-02
## ea_indr_b4_ALL_br_5   1.026088e-02
## ea_indr_b4_ALL_br_6   2.574911e-03
## ea_letter_b4_yng_br_0 7.315496e-02
## ea_letter_b4_yng_br_1 1.966920e-01
## ea_letter_b4_yng_br_2 8.454426e-02
## ea_letter_b4_yng_br_3 2.011330e-02
## ea_letter_b4_yng_br_4 8.420448e-02
## ea_letter_b4_yng_br_5 1.393562e-02
## ea_letter_b4_yng_br_6 1.696551e-03
## ea_tv_b4_yng_br_0     1.031047e-01
## ea_tv_b4_yng_br_1     2.107953e-01
## ea_tv_b4_yng_br_2     8.794403e-02
## ea_tv_b4_yng_br_3     3.745132e-02
## ea_tv_b4_yng_br_4     1.010501e-01
## ea_tv_b4_yng_br_5     4.496574e-03
## ea_tv_b4_yng_br_6     1.062239e-03
## ea_boardg_b4_yng_br_0 4.093013e-02
## ea_boardg_b4_yng_br_1 1.044200e-01
## ea_boardg_b4_yng_br_2 6.381692e-02
## ea_boardg_b4_yng_br_3 4.009893e-05
## ea_boardg_b4_yng_br_4 3.883338e-02
## ea_boardg_b4_yng_br_5 8.612143e-03
## ea_boardg_b4_yng_br_6 4.903465e-03
## ea_vidg_b4_yng_br_0   1.144369e-01
## ea_vidg_b4_yng_br_1   2.099503e-01
## ea_vidg_b4_yng_br_2   9.735189e-02
## ea_vidg_b4_yng_br_3   3.201145e-02
## ea_vidg_b4_yng_br_4   1.098387e-01
## ea_vidg_b4_yng_br_5   5.779317e-03
## ea_vidg_b4_yng_br_6   3.575956e-03
## ea_edapp_b4_yng_br_0  9.333173e-02
## ea_edapp_b4_yng_br_1  1.880057e-01
## ea_edapp_b4_yng_br_2  9.488918e-02
## ea_edapp_b4_yng_br_3  1.862803e-02
## ea_edapp_b4_yng_br_4  9.242454e-02
## ea_edapp_b4_yng_br_5  2.567883e-03
## ea_edapp_b4_yng_br_6  1.013386e-02
var_pref_3$contrib
##                              Dim 1     Dim 2       Dim 3        Dim 4
## ea_read_b4_ALL_br_0   0.6500482318 1.6529903 1.940258687 1.6796479133
## ea_read_b4_ALL_br_1   0.8389151644 0.7896795 0.719123322 0.3110883208
## ea_read_b4_ALL_br_2   0.2335799855 1.3849629 0.658651384 1.2050747869
## ea_read_b4_ALL_br_3   0.0338281200 0.5532830 0.302976091 0.2693272167
## ea_read_b4_ALL_br_4   0.6699685569 2.6233888 1.180952105 0.8885085506
## ea_read_b4_ALL_br_5   6.6680905377 0.7171330 0.792284335 0.3519590670
## ea_read_b4_ALL_br_6   0.4879916949 1.3203024 0.735321553 2.3036687352
## ea_indr_b4_ALL_br_0   0.6762918612 2.0199487 3.852805752 2.7861105994
## ea_indr_b4_ALL_br_1   1.8845453365 1.0024094 2.077096648 0.7132088242
## ea_indr_b4_ALL_br_2   0.3042249509 1.0008474 0.355616448 1.0682727425
## ea_indr_b4_ALL_br_3   0.0496677854 1.5574530 0.135883187 0.1626202393
## ea_indr_b4_ALL_br_4   1.5556414714 2.4434720 0.795073238 1.5688697621
## ea_indr_b4_ALL_br_5   5.4880631275 0.8105058 1.040601833 0.0012298888
## ea_indr_b4_ALL_br_6   0.8823827898 0.9145409 0.733352262 1.9615883485
## ea_letter_b4_yng_br_0 1.3331523026 1.3421496 2.287898325 3.1275714112
## ea_letter_b4_yng_br_1 1.1622119611 0.6468020 1.585028909 0.4643828167
## ea_letter_b4_yng_br_2 0.0001210723 2.0785215 0.604316873 2.8417731282
## ea_letter_b4_yng_br_3 0.2757571509 1.9939049 0.108949419 0.0001237532
## ea_letter_b4_yng_br_4 2.8468037377 2.8510579 1.691004057 0.5884719533
## ea_letter_b4_yng_br_5 9.6270155171 3.6669770 1.813197565 0.0092005817
## ea_letter_b4_yng_br_6 0.8757340390 0.2387496 0.896921297 2.8173867361
## ea_tv_b4_yng_br_0     1.8146015798 2.3439773 2.565688570 4.5805655753
## ea_tv_b4_yng_br_1     2.3917034645 2.1268198 2.148395249 0.0772308752
## ea_tv_b4_yng_br_2     0.5188678190 1.9106324 1.587072556 2.4026571179
## ea_tv_b4_yng_br_3     0.0014962515 1.6543729 0.037228181 1.5142659674
## ea_tv_b4_yng_br_4     2.0160083742 5.5661560 2.227345940 1.4447664367
## ea_tv_b4_yng_br_5     9.5793877713 1.9355489 4.152509010 1.0769607516
## ea_tv_b4_yng_br_6     1.4128911365 2.1207813 6.892852875 7.5224498022
## ea_boardg_b4_yng_br_0 0.6415173265 2.6561534 3.762734664 1.2499025649
## ea_boardg_b4_yng_br_1 1.3318754582 0.5008355 1.906854853 0.0301741206
## ea_boardg_b4_yng_br_2 0.0628264708 1.9503009 1.014683503 1.8309224256
## ea_boardg_b4_yng_br_3 0.8097093879 3.0827928 0.014961923 0.0080712773
## ea_boardg_b4_yng_br_4 1.1614586517 3.8673799 1.844730149 0.2558874029
## ea_boardg_b4_yng_br_5 8.8759465055 2.1651156 3.751816068 1.3901434257
## ea_boardg_b4_yng_br_6 2.2162217965 1.3085094 3.717011563 7.4957200547
## ea_vidg_b4_yng_br_0   1.4702076271 1.9327950 2.443039567 5.1027914039
## ea_vidg_b4_yng_br_1   2.5128034955 2.0340483 2.409886174 0.1559480374
## ea_vidg_b4_yng_br_2   0.4600892744 2.0867291 1.626123188 2.6294646573
## ea_vidg_b4_yng_br_3   0.1641759994 1.3029472 0.000026289 1.5454703294
## ea_vidg_b4_yng_br_4   1.3899006154 5.7188587 2.422211117 2.6522830337
## ea_vidg_b4_yng_br_5   8.4493371306 1.1898423 5.210950775 1.9572118327
## ea_vidg_b4_yng_br_6   1.8980600798 3.3324675 9.000789286 9.7929407240
## ea_edapp_b4_yng_br_0  0.8008444770 1.4197641 1.201509560 3.4011339277
## ea_edapp_b4_yng_br_1  2.1614374805 1.5249770 1.616547117 0.0058497677
## ea_edapp_b4_yng_br_2  0.4655902708 1.0082460 2.326447969 2.5174765863
## ea_edapp_b4_yng_br_3  0.0137817806 1.4414634 0.012316683 1.7122999198
## ea_edapp_b4_yng_br_4  1.3041911543 7.3523338 2.207225700 2.1562654619
## ea_edapp_b4_yng_br_5  6.8941664788 1.4207913 3.721465444 1.7457347602
## ea_edapp_b4_yng_br_6  2.6368667473 3.4362805 5.870262735 8.6253263843
##                             Dim 5
## ea_read_b4_ALL_br_0   1.072253972
## ea_read_b4_ALL_br_1   4.050606540
## ea_read_b4_ALL_br_2   1.420183022
## ea_read_b4_ALL_br_3   0.815001073
## ea_read_b4_ALL_br_4   0.164730831
## ea_read_b4_ALL_br_5   0.217961566
## ea_read_b4_ALL_br_6   0.137348128
## ea_indr_b4_ALL_br_0   1.769774203
## ea_indr_b4_ALL_br_1   4.736761976
## ea_indr_b4_ALL_br_2   2.170447596
## ea_indr_b4_ALL_br_3   0.851809828
## ea_indr_b4_ALL_br_4   1.975187120
## ea_indr_b4_ALL_br_5   0.416015412
## ea_indr_b4_ALL_br_6   0.107631130
## ea_letter_b4_yng_br_0 2.128769615
## ea_letter_b4_yng_br_1 5.860887879
## ea_letter_b4_yng_br_2 2.961670294
## ea_letter_b4_yng_br_3 0.812663677
## ea_letter_b4_yng_br_4 3.278825103
## ea_letter_b4_yng_br_5 0.580563294
## ea_letter_b4_yng_br_6 0.072218033
## ea_tv_b4_yng_br_0     3.554301959
## ea_tv_b4_yng_br_1     7.031333507
## ea_tv_b4_yng_br_2     3.000986765
## ea_tv_b4_yng_br_3     1.463538093
## ea_tv_b4_yng_br_4     3.553990404
## ea_tv_b4_yng_br_5     0.179798151
## ea_tv_b4_yng_br_6     0.043882648
## ea_boardg_b4_yng_br_0 1.305293522
## ea_boardg_b4_yng_br_1 2.965697219
## ea_boardg_b4_yng_br_2 2.137597504
## ea_boardg_b4_yng_br_3 0.001567003
## ea_boardg_b4_yng_br_4 1.579874756
## ea_boardg_b4_yng_br_5 0.359987064
## ea_boardg_b4_yng_br_6 0.206675460
## ea_vidg_b4_yng_br_0   3.992866391
## ea_vidg_b4_yng_br_1   6.812685721
## ea_vidg_b4_yng_br_2   3.315225548
## ea_vidg_b4_yng_br_3   1.239787494
## ea_vidg_b4_yng_br_4   3.970398949
## ea_vidg_b4_yng_br_5   0.232299257
## ea_vidg_b4_yng_br_6   0.146729804
## ea_edapp_b4_yng_br_0  2.989446896
## ea_edapp_b4_yng_br_1  6.336755854
## ea_edapp_b4_yng_br_2  3.343928025
## ea_edapp_b4_yng_br_3  0.709754845
## ea_edapp_b4_yng_br_4  3.405415248
## ea_edapp_b4_yng_br_5  0.104470252
## ea_edapp_b4_yng_br_6  0.414401369

#Correlação entre variáveis e dimensões principais

Pode-se observar que para as duas principais dimensões, a maioria das variáveis têm um correlação alta, destoando as variáveis ea_read_b4_AAL_br e ea_indr_b4_AAL_br.

fviz_mca_var(res.mca_pref_3, choice = "mca.cor", 
            repel = TRUE, # Avoid text overlapping (slow)
            ggtheme = theme_minimal())

head(round(var_pref_3$coord, 2), 4)
##                     Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_b4_ALL_br_0 -0.37  0.52  0.53 -0.48  0.37
## ea_read_b4_ALL_br_1 -0.33  0.29 -0.26  0.16 -0.57
## ea_read_b4_ALL_br_2 -0.23 -0.49 -0.32  0.42  0.44
## ea_read_b4_ALL_br_3 -0.12 -0.45 -0.31  0.29  0.48

Agora visualizaando apenas as categorias de variáveis, ou seja, qual opção o responsável assinalou no questionário, é possível um grupo de alternativas que são bem explicadas pelas principais dimensões e um grupo de alternativas que não são explicadas pelas principais dimensões.

fviz_mca_var(res.mca_pref_3, 
             repel = TRUE, # Avoid text overlapping (slow)
             ggtheme = theme_minimal())
## Warning: ggrepel: 25 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

head(var_pref_3$cos2, 10)
##                           Dim 1      Dim 2       Dim 3       Dim 4       Dim 5
## ea_read_b4_ALL_br_0 0.030083635 0.06138107 0.063923231 0.052225760 0.030608714
## ea_read_b4_ALL_br_1 0.044907281 0.03391791 0.027404157 0.011188303 0.133746206
## ea_read_b4_ALL_br_2 0.010661205 0.05072105 0.021401323 0.036954425 0.039983212
## ea_read_b4_ALL_br_3 0.001393437 0.01828673 0.008884481 0.007453691 0.020707622
## ea_read_b4_ALL_br_4 0.031067416 0.09760967 0.038984984 0.027681787 0.004711816
## ea_read_b4_ALL_br_5 0.268947460 0.02320840 0.022748951 0.009537618 0.005422613
## ea_read_b4_ALL_br_6 0.018769985 0.04074782 0.020134607 0.059532391 0.003258644
## ea_indr_b4_ALL_br_0 0.031360637 0.07515719 0.127186842 0.086802225 0.050621065
## ea_indr_b4_ALL_br_1 0.096862706 0.04134039 0.076001308 0.024629098 0.150173834
## ea_indr_b4_ALL_br_2 0.014135568 0.03731348 0.011762906 0.033348966 0.062205737

Se uma categoria de variável é bem representada por duas dimensões, a soma do cos2 é próxima de um. Para alguns dos itens de linha, são necessárias mais de 2 dimensões para representar perfeitamente os dados, o que indica que essas alternativas fazem parte daquele grupo de alternativas no campo inferior esquerdo do gráfico acima.

fviz_mca_var(res.mca_pref_3, col.var = "cos2",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"), 
             repel = TRUE, # Avoid text overlapping
             ggtheme = theme_minimal())
## Warning: ggrepel: 25 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

fviz_cos2(res.mca_pref_3, choice = "var", axes = 1:2)

head(round(var_pref_3$contrib,2), 49)
##                       Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_b4_ALL_br_0    0.65  1.65  1.94  1.68  1.07
## ea_read_b4_ALL_br_1    0.84  0.79  0.72  0.31  4.05
## ea_read_b4_ALL_br_2    0.23  1.38  0.66  1.21  1.42
## ea_read_b4_ALL_br_3    0.03  0.55  0.30  0.27  0.82
## ea_read_b4_ALL_br_4    0.67  2.62  1.18  0.89  0.16
## ea_read_b4_ALL_br_5    6.67  0.72  0.79  0.35  0.22
## ea_read_b4_ALL_br_6    0.49  1.32  0.74  2.30  0.14
## ea_indr_b4_ALL_br_0    0.68  2.02  3.85  2.79  1.77
## ea_indr_b4_ALL_br_1    1.88  1.00  2.08  0.71  4.74
## ea_indr_b4_ALL_br_2    0.30  1.00  0.36  1.07  2.17
## ea_indr_b4_ALL_br_3    0.05  1.56  0.14  0.16  0.85
## ea_indr_b4_ALL_br_4    1.56  2.44  0.80  1.57  1.98
## ea_indr_b4_ALL_br_5    5.49  0.81  1.04  0.00  0.42
## ea_indr_b4_ALL_br_6    0.88  0.91  0.73  1.96  0.11
## ea_letter_b4_yng_br_0  1.33  1.34  2.29  3.13  2.13
## ea_letter_b4_yng_br_1  1.16  0.65  1.59  0.46  5.86
## ea_letter_b4_yng_br_2  0.00  2.08  0.60  2.84  2.96
## ea_letter_b4_yng_br_3  0.28  1.99  0.11  0.00  0.81
## ea_letter_b4_yng_br_4  2.85  2.85  1.69  0.59  3.28
## ea_letter_b4_yng_br_5  9.63  3.67  1.81  0.01  0.58
## ea_letter_b4_yng_br_6  0.88  0.24  0.90  2.82  0.07
## ea_tv_b4_yng_br_0      1.81  2.34  2.57  4.58  3.55
## ea_tv_b4_yng_br_1      2.39  2.13  2.15  0.08  7.03
## ea_tv_b4_yng_br_2      0.52  1.91  1.59  2.40  3.00
## ea_tv_b4_yng_br_3      0.00  1.65  0.04  1.51  1.46
## ea_tv_b4_yng_br_4      2.02  5.57  2.23  1.44  3.55
## ea_tv_b4_yng_br_5      9.58  1.94  4.15  1.08  0.18
## ea_tv_b4_yng_br_6      1.41  2.12  6.89  7.52  0.04
## ea_boardg_b4_yng_br_0  0.64  2.66  3.76  1.25  1.31
## ea_boardg_b4_yng_br_1  1.33  0.50  1.91  0.03  2.97
## ea_boardg_b4_yng_br_2  0.06  1.95  1.01  1.83  2.14
## ea_boardg_b4_yng_br_3  0.81  3.08  0.01  0.01  0.00
## ea_boardg_b4_yng_br_4  1.16  3.87  1.84  0.26  1.58
## ea_boardg_b4_yng_br_5  8.88  2.17  3.75  1.39  0.36
## ea_boardg_b4_yng_br_6  2.22  1.31  3.72  7.50  0.21
## ea_vidg_b4_yng_br_0    1.47  1.93  2.44  5.10  3.99
## ea_vidg_b4_yng_br_1    2.51  2.03  2.41  0.16  6.81
## ea_vidg_b4_yng_br_2    0.46  2.09  1.63  2.63  3.32
## ea_vidg_b4_yng_br_3    0.16  1.30  0.00  1.55  1.24
## ea_vidg_b4_yng_br_4    1.39  5.72  2.42  2.65  3.97
## ea_vidg_b4_yng_br_5    8.45  1.19  5.21  1.96  0.23
## ea_vidg_b4_yng_br_6    1.90  3.33  9.00  9.79  0.15
## ea_edapp_b4_yng_br_0   0.80  1.42  1.20  3.40  2.99
## ea_edapp_b4_yng_br_1   2.16  1.52  1.62  0.01  6.34
## ea_edapp_b4_yng_br_2   0.47  1.01  2.33  2.52  3.34
## ea_edapp_b4_yng_br_3   0.01  1.44  0.01  1.71  0.71
## ea_edapp_b4_yng_br_4   1.30  7.35  2.21  2.16  3.41
## ea_edapp_b4_yng_br_5   6.89  1.42  3.72  1.75  0.10
## ea_edapp_b4_yng_br_6   2.64  3.44  5.87  8.63  0.41

15 principais categorias de variáveis que contribuem para cada uma das principais dimensões:

# Contributions of rows to dimension 1
fviz_contrib(res.mca_pref_3, choice = "var", axes = 1, top = 15)

# Contributions of rows to dimension 2
fviz_contrib(res.mca_pref_3, choice = "var", axes = 2, top = 15)

15 principais categorias de variáveis que contribuem as duas principais dimensões combinadas:

fviz_contrib(res.mca_pref_3, choice = "var", axes = 1:2, top = 15)

fviz_mca_var(res.mca_pref_3, alpha.var="contrib",
             repel = TRUE,
             ggtheme = theme_minimal())
## Warning: ggrepel: 25 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

##Gráfico de indivíduos Resultados

lista contendo as coordenadas, o cos2 e as contribuições dos indivíduos:

ind_pref_3 <- get_mca_ind(res.mca_pref_3)
ind_pref_3
## Multiple Correspondence Analysis Results for individuals
##  ===================================================
##   Name       Description                       
## 1 "$coord"   "Coordinates for the individuals" 
## 2 "$cos2"    "Cos2 for the individuals"        
## 3 "$contrib" "contributions of the individuals"

colorir os indivíduos por seus valores de cos2:

fviz_mca_ind(res.mca_pref_3, col.ind = "cos2", 
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE, # Avoid text overlapping (slow if many points)
             ggtheme = theme_minimal())
## Warning: ggrepel: 534 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

# Cos2 of individuals
fviz_cos2(res.mca_pref_3, choice = "ind", axes = 1:2, top = 20)

# Contribution of individuals to the dimensions
fviz_contrib(res.mca_pref_3, choice = "ind", axes = 1:2, top = 20)

POS-PANDEMIA

db_bloco_3_posf <- as.data.frame(lapply(db_bloco_3_pos, as.factor))
summary(db_bloco_3_posf)
##  ea_read_since_yng_br ea_indr_since_yng_br ea_letter_since_yng_br
##  0:153                0:121                0:206                 
##  1:156                1:165                1:157                 
##  2: 91                2: 86                2: 98                 
##  3: 64                3: 65                3: 55                 
##  4: 98                4:112                4: 71                 
##  5: 32                5: 36                5: 14                 
##  6: 21                6: 30                6: 14                 
##  ea_tv_since_yng_br ea_boardg_since_yng_br ea_vidg_since_yng_br
##  0: 90              0:126                  0: 70               
##  1:121              1:188                  1:115               
##  2: 97              2:144                  2: 99               
##  3: 77              3: 65                  3: 73               
##  4:140              4: 56                  4:141               
##  5: 43              5: 20                  5: 59               
##  6: 47              6: 16                  6: 58               
##  ea_edapp_since_yng_br
##  0:147                
##  1: 99                
##  2: 76                
##  3: 62                
##  4:126                
##  5: 53                
##  6: 52

Podemos observar nos gráficos abaixo a frequência absoluta respostas para cada categoria de cada pergunta.

for (i in 1:7) {
  plot(db_bloco_3_posf[,i], main=colnames(db_bloco_3_posf)[i],
       ylab = "Count", col="steelblue", las = 2)
  }

MCA(db_bloco_3_posf,ncp = 5, graph = TRUE) 
## Warning: ggrepel: 32 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 7 variables
## *The results are available in the following objects:
## 
##    name              description                       
## 1  "$eig"            "eigenvalues"                     
## 2  "$var"            "results for the variables"       
## 3  "$var$coord"      "coord. of the categories"        
## 4  "$var$cos2"       "cos2 for the categories"         
## 5  "$var$contrib"    "contributions of the categories" 
## 6  "$var$v.test"     "v-test for the categories"       
## 7  "$ind"            "results for the individuals"     
## 8  "$ind$coord"      "coord. for the individuals"      
## 9  "$ind$cos2"       "cos2 for the individuals"        
## 10 "$ind$contrib"    "contributions of the individuals"
## 11 "$call"           "intermediate results"            
## 12 "$call$marge.col" "weights of columns"              
## 13 "$call$marge.li"  "weights of rows"
res.mca_posf_3 <- MCA(db_bloco_3_posf, graph = FALSE)
print(res.mca_posf_3)
## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 7 variables
## *The results are available in the following objects:
## 
##    name              description                       
## 1  "$eig"            "eigenvalues"                     
## 2  "$var"            "results for the variables"       
## 3  "$var$coord"      "coord. of the categories"        
## 4  "$var$cos2"       "cos2 for the categories"         
## 5  "$var$contrib"    "contributions of the categories" 
## 6  "$var$v.test"     "v-test for the categories"       
## 7  "$ind"            "results for the individuals"     
## 8  "$ind$coord"      "coord. for the individuals"      
## 9  "$ind$cos2"       "cos2 for the individuals"        
## 10 "$ind$contrib"    "contributions of the individuals"
## 11 "$call"           "intermediate results"            
## 12 "$call$marge.col" "weights of columns"              
## 13 "$call$marge.li"  "weights of rows"

#Visualização e interpretação Autovalores/variâncias retidos por cada dimensão (eixo). Pode-se observar que 54% da variância é explicada pelas primeiras 10 dimensões

eig.val_posf_3 <- get_eigenvalue(res.mca_posf_3)
eig.val_posf_3
##        eigenvalue variance.percent cumulative.variance.percent
## Dim.1  0.55091374        9.1818956                    9.181896
## Dim.2  0.49805913        8.3009855                   17.482881
## Dim.3  0.42119891        7.0199818                   24.502863
## Dim.4  0.41994189        6.9990314                   31.501894
## Dim.5  0.35137285        5.8562142                   37.358109
## Dim.6  0.28992657        4.8321095                   42.190218
## Dim.7  0.21342736        3.5571226                   45.747341
## Dim.8  0.18172303        3.0287172                   48.776058
## Dim.9  0.17345913        2.8909854                   51.667043
## Dim.10 0.15594025        2.5990041                   54.266047
## Dim.11 0.15179133        2.5298554                   56.795903
## Dim.12 0.14572539        2.4287565                   59.224659
## Dim.13 0.14155815        2.3593026                   61.583962
## Dim.14 0.13939213        2.3232021                   63.907164
## Dim.15 0.12599827        2.0999712                   66.007135
## Dim.16 0.12455524        2.0759206                   68.083056
## Dim.17 0.12170844        2.0284740                   70.111530
## Dim.18 0.12041429        2.0069048                   72.118435
## Dim.19 0.11042161        1.8403601                   73.958795
## Dim.20 0.10876704        1.8127841                   75.771579
## Dim.21 0.10400710        1.7334516                   77.505031
## Dim.22 0.09767326        1.6278877                   79.132918
## Dim.23 0.09552075        1.5920125                   80.724931
## Dim.24 0.08985304        1.4975507                   82.222481
## Dim.25 0.08808931        1.4681552                   83.690637
## Dim.26 0.08535544        1.4225906                   85.113227
## Dim.27 0.08080926        1.3468210                   86.460048
## Dim.28 0.08049425        1.3415708                   87.801619
## Dim.29 0.07538253        1.2563755                   89.057995
## Dim.30 0.07223514        1.2039190                   90.261914
## Dim.31 0.07108740        1.1847900                   91.446704
## Dim.32 0.06640850        1.1068084                   92.553512
## Dim.33 0.06266182        1.0443636                   93.597875
## Dim.34 0.05621992        0.9369986                   94.534874
## Dim.35 0.05405585        0.9009309                   95.435805
## Dim.36 0.04958513        0.8264188                   96.262224
## Dim.37 0.04723205        0.7872008                   97.049425
## Dim.38 0.04258798        0.7097997                   97.759224
## Dim.39 0.04031902        0.6719836                   98.431208
## Dim.40 0.03436495        0.5727492                   99.003957
## Dim.41 0.03121637        0.5202728                   99.524230
## Dim.42 0.02854620        0.4757700                  100.000000

Porcentagens de inércia explicadas por cada dimensão MCA

fviz_screeplot(res.mca_posf_3, addlabels = TRUE, ylim = c(0, 10))

Biplot de indivíduos e categorias de variáveis:

Os pontos azuis representam indivíduos e os pontos vermelhos representam as variáveis, quanto mais próximos uns dos outros mais similares são essas observações. Podemos observar um cluster de indivíduos que não são explicados pela dimensão 1 e nem pela dimensãso 2 no canto inferior esquerdo do gráfico.

fviz_mca_biplot(res.mca_posf_3, 
               repel = TRUE, # Avoid text overlapping (slow if many point)
               ggtheme = theme_minimal())
## Warning: ggrepel: 526 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 27 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

#Gráfico de variáveis #Resultados

Usada para extrair os resultados para categorias de variáveis. Esta função retorna uma lista contendo as coordenadas, o cos2 e a contribuição das categorias de variáveis:

var_posf_3 <- get_mca_var(res.mca_posf_3)
var_posf_3
## Multiple Correspondence Analysis Results for variables
##  ===================================================
##   Name       Description                  
## 1 "$coord"   "Coordinates for categories" 
## 2 "$cos2"    "Cos2 for categories"        
## 3 "$contrib" "contributions of categories"
# Coordinates
var_posf_3$coord
##                                Dim 1      Dim 2      Dim 3       Dim 4
## ea_read_since_yng_br_0   -0.38947982  0.5041055 -0.1829279 -0.31697315
## ea_read_since_yng_br_1   -0.52466392  0.1824891  0.2256815 -0.05516922
## ea_read_since_yng_br_2   -0.19403149 -0.4985289  0.1723880  0.58306643
## ea_read_since_yng_br_3    0.22486292 -0.5022598  0.3060534  0.81631808
## ea_read_since_yng_br_4    0.41683575 -0.8059556 -0.7284819 -0.55038663
## ea_read_since_yng_br_5    1.73930013  0.1796019  1.6813529 -0.87761728
## ea_read_since_yng_br_6    2.29505313  2.1500334 -1.1859586  1.61054906
## ea_indr_since_yng_br_0   -0.49393341  0.5179402 -0.2759620 -0.43131671
## ea_indr_since_yng_br_1   -0.52417652  0.3060655  0.2959020  0.01164216
## ea_indr_since_yng_br_2   -0.17394904 -0.6087357  0.2314559  0.67811340
## ea_indr_since_yng_br_3    0.31038439 -0.5617723  0.1942725  0.54100984
## ea_indr_since_yng_br_4    0.37247161 -0.7016744 -0.5671506 -0.52095279
## ea_indr_since_yng_br_5    1.60608841  0.3490682  1.3263018 -0.79574706
## ea_indr_since_yng_br_6    1.38345656  1.3905325 -1.0730450  1.45928601
## ea_letter_since_yng_br_0 -0.42789894  0.5072968 -0.2480182 -0.41588788
## ea_letter_since_yng_br_1 -0.48127883  0.1606773  0.3777639  0.19031294
## ea_letter_since_yng_br_2  0.02793906 -0.6174997  0.3184133  0.63631891
## ea_letter_since_yng_br_3  0.42307855 -0.7716526  0.1939514  0.92996815
## ea_letter_since_yng_br_4  0.76860991 -1.0266130 -1.0143352 -0.87032389
## ea_letter_since_yng_br_5  3.09042712  0.7263406  2.9169621 -1.63079891
## ea_letter_since_yng_br_6  2.84738039  2.5676528 -1.3506203  1.92217494
## ea_tv_since_yng_br_0     -0.83760867  0.8968317 -0.2721652 -0.76887895
## ea_tv_since_yng_br_1     -0.73515564  0.3828409  0.4571679 -0.03072843
## ea_tv_since_yng_br_2     -0.38700969 -0.4131912  0.4179814  0.70732483
## ea_tv_since_yng_br_3      0.07266170 -0.5796236  0.3174421  1.12612066
## ea_tv_since_yng_br_4      0.38345415 -0.9169333 -0.7784602 -0.55921879
## ea_tv_since_yng_br_5      1.83819479  0.2184887  1.6256977 -1.08322677
## ea_tv_since_yng_br_6      1.35229031  1.6308003 -1.2070268  0.90350697
## ea_boardg_since_yng_br_0 -0.45144941  0.6104810 -0.3198838 -0.59774538
## ea_boardg_since_yng_br_1 -0.48107973  0.2904157  0.1905918  0.04811567
## ea_boardg_since_yng_br_2 -0.13972254 -0.4961347  0.3288134  0.54607457
## ea_boardg_since_yng_br_3  0.45809211 -0.8142901 -0.1294077  0.46519425
## ea_boardg_since_yng_br_4  0.73178634 -1.1136746 -1.0241459 -0.90195047
## ea_boardg_since_yng_br_5  2.92813416  0.7438877  2.6360872 -1.36614101
## ea_boardg_since_yng_br_6  2.38293473  2.5213455 -1.8645690  2.20186597
## ea_vidg_since_yng_br_0   -0.89780568  0.9394967 -0.2459625 -0.88306753
## ea_vidg_since_yng_br_1   -0.76096614  0.4676019  0.4023607 -0.04254611
## ea_vidg_since_yng_br_2   -0.35657469 -0.3724440  0.5134509  0.85408861
## ea_vidg_since_yng_br_3    0.05828689 -0.5929698  0.2744427  0.96893264
## ea_vidg_since_yng_br_4    0.20380860 -0.9343988 -0.7864771 -0.50044612
## ea_vidg_since_yng_br_5    1.50583892  0.2021363  1.2526532 -0.89318316
## ea_vidg_since_yng_br_6    1.10037838  1.3869646 -1.0850574  0.59795489
## ea_edapp_since_yng_br_0  -0.58074031  0.5338623 -0.1833380 -0.54980688
## ea_edapp_since_yng_br_1  -0.75278568  0.4440752  0.4377963  0.09484225
## ea_edapp_since_yng_br_2  -0.39439896 -0.3250220  0.5249969  0.86833445
## ea_edapp_since_yng_br_3   0.23074024 -0.6301666  0.3011992  1.06246607
## ea_edapp_since_yng_br_4   0.24098780 -1.0500031 -0.7009088 -0.38756012
## ea_edapp_since_yng_br_5   1.57189270  0.1625114  1.3503295 -0.81831845
## ea_edapp_since_yng_br_6   1.19015879  1.2503477 -1.1195810  0.61094934
##                                 Dim 5
## ea_read_since_yng_br_0    0.468990546
## ea_read_since_yng_br_1   -0.530947483
## ea_read_since_yng_br_2   -0.004186454
## ea_read_since_yng_br_3    0.551151378
## ea_read_since_yng_br_4   -0.257401820
## ea_read_since_yng_br_5    0.169616156
## ea_read_since_yng_br_6   -0.191562175
## ea_indr_since_yng_br_0    0.766150360
## ea_indr_since_yng_br_1   -0.605463955
## ea_indr_since_yng_br_2    0.093251144
## ea_indr_since_yng_br_3    0.594598315
## ea_indr_since_yng_br_4   -0.329895174
## ea_indr_since_yng_br_5    0.069821656
## ea_indr_since_yng_br_6   -0.167881665
## ea_letter_since_yng_br_0  0.520596470
## ea_letter_since_yng_br_1 -0.820898873
## ea_letter_since_yng_br_2  0.186589119
## ea_letter_since_yng_br_3  0.901530849
## ea_letter_since_yng_br_4 -0.572207190
## ea_letter_since_yng_br_5  0.235479896
## ea_letter_since_yng_br_6 -0.635834876
## ea_tv_since_yng_br_0      1.056004016
## ea_tv_since_yng_br_1     -1.017491115
## ea_tv_since_yng_br_2      0.031690016
## ea_tv_since_yng_br_3      0.874586946
## ea_tv_since_yng_br_4     -0.251497535
## ea_tv_since_yng_br_5      0.171173528
## ea_tv_since_yng_br_6     -0.308337653
## ea_boardg_since_yng_br_0  0.710527684
## ea_boardg_since_yng_br_1 -0.587300036
## ea_boardg_since_yng_br_2  0.172168840
## ea_boardg_since_yng_br_3  0.490547178
## ea_boardg_since_yng_br_4 -0.492267425
## ea_boardg_since_yng_br_5  0.121034343
## ea_boardg_since_yng_br_6 -0.665354497
## ea_vidg_since_yng_br_0    1.154351643
## ea_vidg_since_yng_br_1   -0.968525267
## ea_vidg_since_yng_br_2   -0.065173327
## ea_vidg_since_yng_br_3    1.071846292
## ea_vidg_since_yng_br_4   -0.240465217
## ea_vidg_since_yng_br_5    0.108776831
## ea_vidg_since_yng_br_6   -0.236708047
## ea_edapp_since_yng_br_0   0.611239207
## ea_edapp_since_yng_br_1  -1.000529691
## ea_edapp_since_yng_br_2  -0.070219176
## ea_edapp_since_yng_br_3   1.034718797
## ea_edapp_since_yng_br_4  -0.240350738
## ea_edapp_since_yng_br_5  -0.052801031
## ea_edapp_since_yng_br_6  -0.317942007
var_posf_3$cos2
##                                 Dim 1       Dim 2       Dim 3        Dim 4
## ea_read_since_yng_br_0   0.0502365007 0.084157395 0.011081780 3.327319e-02
## ea_read_since_yng_br_1   0.0935565745 0.011318424 0.017310272 1.034441e-03
## ea_read_since_yng_br_2   0.0065381451 0.043160932 0.005160884 5.903998e-02
## ea_read_since_yng_br_3   0.0058730547 0.029301186 0.010879850 7.740111e-02
## ea_read_since_yng_br_4   0.0329355902 0.123128268 0.100594242 5.742107e-02
## ea_read_since_yng_br_5   0.1660467902 0.001770531 0.155166925 4.227579e-02
## ea_read_since_yng_br_6   0.1862165766 0.163426798 0.049724666 9.170241e-02
## ea_indr_since_yng_br_0   0.0597578865 0.065707910 0.018653360 4.556706e-02
## ea_indr_since_yng_br_1   0.1007457089 0.034347896 0.032104602 4.969797e-05
## ea_indr_since_yng_br_2   0.0049191133 0.060242135 0.008709223 7.475624e-02
## ea_indr_since_yng_br_3   0.0113854553 0.037296772 0.004460395 3.459083e-02
## ea_indr_since_yng_br_4   0.0308913152 0.109627946 0.071622060 6.042919e-02
## ea_indr_since_yng_br_5   0.1603846630 0.007576079 0.109372635 3.937078e-02
## ea_indr_since_yng_br_6   0.0981513871 0.099157984 0.059047467 1.092059e-01
## ea_letter_since_yng_br_0 0.0922202585 0.129618850 0.030982112 8.711570e-02
## ea_letter_since_yng_br_1 0.0794013128 0.008850001 0.048918718 1.241569e-02
## ea_letter_since_yng_br_2 0.0001479651 0.072278490 0.019218426 7.675120e-02
## ea_letter_since_yng_br_3 0.0175799116 0.058481474 0.003694543 8.493972e-02
## ea_letter_since_yng_br_4 0.0771030235 0.137553914 0.134283425 9.886015e-02
## ea_letter_since_yng_br_5 0.2224797953 0.012289499 0.198205243 6.195187e-02
## ea_letter_since_yng_br_6 0.1888619820 0.153576996 0.042493264 8.606754e-02
## ea_tv_since_yng_br_0     0.1202722768 0.137881224 0.012698380 1.013443e-01
## ea_tv_since_yng_br_1     0.1323783638 0.035900053 0.051192922 2.312805e-04
## ea_tv_since_yng_br_2     0.0280469511 0.031970108 0.032715675 9.368710e-02
## ea_tv_since_yng_br_3     0.0007556480 0.048084005 0.014422399 1.815007e-01
## ea_tv_since_yng_br_4     0.0433372466 0.247804939 0.178610621 9.217177e-02
## ea_tv_since_yng_br_5     0.2540127350 0.003588645 0.198679017 8.820865e-02
## ea_tv_since_yng_br_6     0.1513175817 0.220065059 0.120554483 6.754801e-02
## ea_boardg_since_yng_br_0 0.0525145769 0.096029794 0.026366117 9.206491e-02
## ea_boardg_since_yng_br_1 0.1018976309 0.037133860 0.015993310 1.019302e-03
## ea_boardg_since_yng_br_2 0.0059686284 0.075255946 0.033055271 9.116864e-02
## ea_boardg_since_yng_br_3 0.0248002636 0.078362626 0.001979115 2.557522e-02
## ea_boardg_since_yng_br_4 0.0536469221 0.124248995 0.105075113 8.149700e-02
## ea_boardg_since_yng_br_5 0.2882006602 0.018600635 0.233578337 6.273416e-02
## ea_boardg_since_yng_br_6 0.1516762047 0.169807895 0.092864579 1.295015e-01
## ea_vidg_since_yng_br_0   0.1035300039 0.113368419 0.007770326 1.001589e-01
## ea_vidg_since_yng_br_1   0.1331859777 0.050289856 0.037235650 4.163394e-04
## ea_vidg_since_yng_br_2   0.0243941961 0.026613831 0.050580522 1.399559e-01
## ea_vidg_since_yng_br_3   0.0004575782 0.047357500 0.010144412 1.264476e-01
## ea_vidg_since_yng_br_4   0.0123562241 0.259719954 0.183997919 7.449985e-02
## ea_vidg_since_yng_br_5   0.2406214036 0.004335766 0.166509458 8.465610e-02
## ea_vidg_since_yng_br_6   0.1260831044 0.200310436 0.122596541 3.723142e-02
## ea_edapp_since_yng_br_0  0.1059340121 0.089522054 0.010557869 9.494931e-02
## ea_edapp_since_yng_br_1  0.1087246930 0.037835419 0.036773053 1.725795e-03
## ea_edapp_since_yng_br_2  0.0219329144 0.014895338 0.038863172 1.063161e-01
## ea_edapp_since_yng_br_3  0.0059691605 0.044522275 0.010171245 1.265601e-01
## ea_edapp_since_yng_br_4  0.0149641412 0.284081427 0.126585734 3.870257e-02
## ea_edapp_since_yng_br_5  0.2330157895 0.002490617 0.171956680 6.315158e-02
## ea_edapp_since_yng_br_6  0.1308292231 0.144396469 0.115772660 3.447509e-02
##                                 Dim 5
## ea_read_since_yng_br_0   7.284129e-02
## ea_read_since_yng_br_1   9.581093e-02
## ea_read_since_yng_br_2   3.043706e-06
## ea_read_since_yng_br_3   3.528338e-02
## ea_read_since_yng_br_4   1.255911e-02
## ea_read_since_yng_br_5   1.579123e-03
## ea_read_since_yng_br_6   1.297336e-03
## ea_indr_since_yng_br_0   1.437760e-01
## ea_indr_since_yng_br_1   1.344151e-01
## ea_indr_since_yng_br_2   1.413680e-03
## ea_indr_since_yng_br_3   4.178285e-02
## ea_indr_since_yng_br_4   2.423271e-02
## ea_indr_since_yng_br_5   3.031128e-04
## ea_indr_since_yng_br_6   1.445346e-03
## ea_letter_since_yng_br_0 1.365043e-01
## ea_letter_since_yng_br_1 2.310008e-01
## ea_letter_since_yng_br_2 6.599456e-03
## ea_letter_since_yng_br_3 7.982443e-02
## ea_letter_since_yng_br_4 4.273326e-02
## ea_letter_since_yng_br_5 1.291699e-03
## ea_letter_since_yng_br_6 9.417644e-03
## ea_tv_since_yng_br_0     1.911676e-01
## ea_tv_since_yng_br_1     2.535827e-01
## ea_tv_since_yng_br_2     1.880559e-04
## ea_tv_since_yng_br_3     1.094749e-01
## ea_tv_since_yng_br_4     1.864240e-02
## ea_tv_since_yng_br_5     2.202651e-03
## ea_tv_since_yng_br_6     7.866882e-03
## ea_boardg_since_yng_br_0 1.300839e-01
## ea_boardg_since_yng_br_1 1.518623e-01
## ea_boardg_since_yng_br_2 9.062556e-03
## ea_boardg_since_yng_br_3 2.843886e-02
## ea_boardg_since_yng_br_4 2.427607e-02
## ea_boardg_since_yng_br_5 4.924139e-04
## ea_boardg_since_yng_br_6 1.182495e-02
## ea_vidg_since_yng_br_0   1.711503e-01
## ea_vidg_since_yng_br_1   2.157495e-01
## ea_vidg_since_yng_br_2   8.149393e-04
## ea_vidg_since_yng_br_3   1.547350e-01
## ea_vidg_since_yng_br_4   1.720067e-02
## ea_vidg_since_yng_br_5   1.255596e-03
## ea_vidg_since_yng_br_6   5.834435e-03
## ea_edapp_since_yng_br_0  1.173529e-01
## ea_edapp_since_yng_br_1  1.920638e-01
## ea_edapp_since_yng_br_2  6.952425e-04
## ea_edapp_since_yng_br_3  1.200359e-01
## ea_edapp_since_yng_br_4  1.488513e-02
## ea_edapp_since_yng_br_5  2.629204e-04
## ea_edapp_since_yng_br_6  9.336643e-03
var_posf_3$contrib
##                               Dim 1      Dim 2     Dim 3       Dim 4
## ea_read_since_yng_br_0   0.97859862 1.81334400 0.2823522 0.850303642
## ea_read_since_yng_br_1   1.81063220 0.24229507 0.4381837 0.026263699
## ea_read_since_yng_br_2   0.14445384 1.05479495 0.1491403 1.711256515
## ea_read_since_yng_br_3   0.13644540 0.75297878 0.3306086 2.359046235
## ea_read_since_yng_br_4   0.71795832 2.96889099 2.8681593 1.642099500
## ea_read_since_yng_br_5   4.08171128 0.04814132 4.9889289 1.363320471
## ea_read_since_yng_br_6   4.66388707 4.52747078 1.6289132 3.013039765
## ea_indr_since_yng_br_0   1.24470209 1.51387683 0.5081871 1.245132580
## ea_indr_since_yng_br_1   1.91153526 0.72087273 0.7967451 0.001237054
## ea_indr_since_yng_br_2   0.10971999 1.48628455 0.2540824 2.187463212
## ea_indr_since_yng_br_3   0.26403186 0.95670903 0.1352931 1.052352722
## ea_indr_since_yng_br_4   0.65516038 2.57178821 1.9868011 1.681327839
## ea_indr_since_yng_br_5   3.91547668 0.20458274 3.4924231 1.260927837
## ea_indr_since_yng_br_6   2.42100424 2.70538759 1.9050077 3.533781873
## ea_letter_since_yng_br_0 1.59035063 2.47250632 0.6988339 1.970867328
## ea_letter_since_yng_br_1 1.53333272 0.18904045 1.2356064 0.314538606
## ea_letter_since_yng_br_2 0.00322547 1.74279197 0.5479589 2.194893182
## ea_letter_since_yng_br_3 0.41509543 1.52739820 0.1141007 2.631094750
## ea_letter_since_yng_br_4 1.76853456 3.48993864 4.0286628 2.974800206
## ea_letter_since_yng_br_5 5.63778215 0.34447252 6.5694531 2.059522364
## ea_letter_since_yng_br_6 4.78588498 4.30473656 1.4084265 2.861221675
## ea_tv_since_yng_br_0     2.66236796 3.37606168 0.3676607 2.943039530
## ea_tv_since_yng_br_1     2.75732019 0.82711897 1.3946861 0.006319804
## ea_tv_since_yng_br_2     0.61257414 0.77236004 0.9345989 2.684399729
## ea_tv_since_yng_br_3     0.01714135 1.20650422 0.4279169 5.401299099
## ea_tv_since_yng_br_4     0.86795691 5.48971131 4.6788648 2.421749397
## ea_tv_since_yng_br_5     6.12625065 0.09573538 6.2674033 2.790903846
## ea_tv_since_yng_br_6     3.62393976 5.82968873 3.7763419 2.122259683
## ea_boardg_since_yng_br_0 1.08275942 2.19008412 0.7110410 2.490236831
## ea_boardg_since_yng_br_1 1.83457387 0.73950977 0.3766221 0.024075123
## ea_boardg_since_yng_br_2 0.11853284 1.65313250 0.8586205 2.375221089
## ea_boardg_since_yng_br_3 0.57512498 2.01009976 0.0600307 0.778071810
## ea_boardg_since_yng_br_4 1.26444476 3.23929599 3.2393008 2.519944969
## ea_boardg_since_yng_br_5 7.23028105 0.51616752 7.6645937 2.064708297
## ea_boardg_since_yng_br_6 3.83078270 4.74384664 3.0677257 4.290811059
## ea_vidg_since_yng_br_0   2.37906318 2.88160556 0.2335476 3.019419972
## ea_vidg_since_yng_br_1   2.80783608 1.17272542 1.0267591 0.011514767
## ea_vidg_since_yng_br_2   0.53073711 0.64047625 1.4393706 3.994649721
## ea_vidg_since_yng_br_3   0.01045702 1.19710983 0.3032255 3.790946695
## ea_vidg_since_yng_br_4   0.24694905 5.74155590 4.8098426 1.953311062
## ea_vidg_since_yng_br_5   5.64095051 0.11243112 5.1056767 2.603578730
## ea_vidg_since_yng_br_6   2.96111539 5.20360966 3.7659368 1.147103204
## ea_edapp_since_yng_br_0  2.09037651 1.95398772 0.2724970 2.457962887
## ea_edapp_since_yng_br_1  2.36549010 0.91052984 1.0464513 0.049257977
## ea_edapp_since_yng_br_2  0.49845775 0.37444245 1.1552251 3.169752165
## ea_edapp_since_yng_br_3  0.13918154 1.14828163 0.3101979 3.871323609
## ea_edapp_since_yng_br_4  0.30853462 6.47884572 3.4137619 1.046854560
## ea_edapp_since_yng_br_5  5.52159957 0.06528137 5.3296044 1.963171338
## ea_edapp_since_yng_br_6  3.10567781 3.79149867 3.5946285 1.073621989
##                                 Dim 5
## ea_read_since_yng_br_0   2.2247347981
## ea_read_since_yng_br_1   2.9072767250
## ea_read_since_yng_br_2   0.0001054369
## ea_read_since_yng_br_3   1.2852286832
## ea_read_since_yng_br_4   0.4292476682
## ea_read_since_yng_br_5   0.0608615558
## ea_read_since_yng_br_6   0.0509445176
## ea_indr_since_yng_br_0   4.6953938981
## ea_indr_since_yng_br_1   3.9987032132
## ea_indr_since_yng_br_2   0.0494385161
## ea_indr_since_yng_br_3   1.5192153705
## ea_indr_since_yng_br_4   0.8058024607
## ea_indr_since_yng_br_5   0.0116022289
## ea_indr_since_yng_br_6   0.0558967335
## ea_letter_since_yng_br_0 3.6908661828
## ea_letter_since_yng_br_1 6.9941929912
## ea_letter_since_yng_br_2 0.2255575382
## ea_letter_since_yng_br_3 2.9551703052
## ea_letter_since_yng_br_4 1.5368221490
## ea_letter_since_yng_br_5 0.0513209097
## ea_letter_since_yng_br_6 0.3741755167
## ea_tv_since_yng_br_0     6.6348680373
## ea_tv_since_yng_br_1     8.2814286075
## ea_tv_since_yng_br_2     0.0064398434
## ea_tv_since_yng_br_3     3.8936359910
## ea_tv_since_yng_br_4     0.5854019200
## ea_tv_since_yng_br_5     0.0832914267
## ea_tv_since_yng_br_6     0.2953994107
## ea_boardg_since_yng_br_0 4.2052439333
## ea_boardg_since_yng_br_1 4.2868328039
## ea_boardg_since_yng_br_2 0.2821826066
## ea_boardg_since_yng_br_3 1.0340310053
## ea_boardg_since_yng_br_4 0.8971165393
## ea_boardg_since_yng_br_5 0.0193689407
## ea_boardg_since_yng_br_6 0.4682575802
## ea_vidg_since_yng_br_0   6.1664175872
## ea_vidg_since_yng_br_1   7.1314590185
## ea_vidg_since_yng_br_2   0.0277992846
## ea_vidg_since_yng_br_3   5.5442974297
## ea_vidg_since_yng_br_4   0.5389919566
## ea_vidg_since_yng_br_5   0.0461512490
## ea_vidg_since_yng_br_6   0.2148387802
## ea_edapp_since_yng_br_0  3.6307670280
## ea_edapp_since_yng_br_1  6.5516968948
## ea_edapp_since_yng_br_2  0.0247732903
## ea_edapp_since_yng_br_3  4.3882868371
## ea_edapp_since_yng_br_4  0.4811938933
## ea_edapp_since_yng_br_5  0.0097683073
## ea_edapp_since_yng_br_6  0.3475023989

#Correlação entre variáveis e dimensões principais

correlação entre as variáveis e as dimensões principais do MCA Pode-se observar que para as duas principais dimensões, a maioria das variáveis têm um correlação alta, destoando um pouco as variáveis ea_read_since_yng_br e ea_indr_since_yng_br.

fviz_mca_var(res.mca_posf_3, choice = "mca.cor", 
            repel = TRUE, # Avoid text overlapping (slow)
            ggtheme = theme_minimal())

head(round(var_posf_3$coord, 2), 4)
##                        Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_since_yng_br_0 -0.39  0.50 -0.18 -0.32  0.47
## ea_read_since_yng_br_1 -0.52  0.18  0.23 -0.06 -0.53
## ea_read_since_yng_br_2 -0.19 -0.50  0.17  0.58  0.00
## ea_read_since_yng_br_3  0.22 -0.50  0.31  0.82  0.55

Agora visualizando apenas as categorias de variáveis, ou seja, qual opção o responsável assinalou no questionárioas pelas principais dimensões.

fviz_mca_var(res.mca_posf_3, 
             repel = TRUE, # Avoid text overlapping (slow)
             ggtheme = theme_minimal())
## Warning: ggrepel: 24 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

head(var_posf_3$cos2, 10)
##                              Dim 1       Dim 2       Dim 3        Dim 4
## ea_read_since_yng_br_0 0.050236501 0.084157395 0.011081780 3.327319e-02
## ea_read_since_yng_br_1 0.093556574 0.011318424 0.017310272 1.034441e-03
## ea_read_since_yng_br_2 0.006538145 0.043160932 0.005160884 5.903998e-02
## ea_read_since_yng_br_3 0.005873055 0.029301186 0.010879850 7.740111e-02
## ea_read_since_yng_br_4 0.032935590 0.123128268 0.100594242 5.742107e-02
## ea_read_since_yng_br_5 0.166046790 0.001770531 0.155166925 4.227579e-02
## ea_read_since_yng_br_6 0.186216577 0.163426798 0.049724666 9.170241e-02
## ea_indr_since_yng_br_0 0.059757887 0.065707910 0.018653360 4.556706e-02
## ea_indr_since_yng_br_1 0.100745709 0.034347896 0.032104602 4.969797e-05
## ea_indr_since_yng_br_2 0.004919113 0.060242135 0.008709223 7.475624e-02
##                               Dim 5
## ea_read_since_yng_br_0 7.284129e-02
## ea_read_since_yng_br_1 9.581093e-02
## ea_read_since_yng_br_2 3.043706e-06
## ea_read_since_yng_br_3 3.528338e-02
## ea_read_since_yng_br_4 1.255911e-02
## ea_read_since_yng_br_5 1.579123e-03
## ea_read_since_yng_br_6 1.297336e-03
## ea_indr_since_yng_br_0 1.437760e-01
## ea_indr_since_yng_br_1 1.344151e-01
## ea_indr_since_yng_br_2 1.413680e-03

Se uma categoria de variável é bem representada por duas dimensões, a soma do cos2 é próxima de um. Para alguns dos itens de linha, são necessárias mais de 2 dimensões para representar perfeitamente os dados.

fviz_mca_var(res.mca_posf_3, col.var = "cos2",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"), 
             repel = TRUE, # Avoid text overlapping
             ggtheme = theme_minimal())
## Warning: ggrepel: 24 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

15 principais categorias de variáveis que contribuem para cada uma das principais dimensões:

# Contributions of rows to dimension 1
fviz_contrib(res.mca_pref_3, choice = "var", axes = 1, top = 15)

fviz_contrib(res.mca_posf_3, choice = "var", axes = 1, top = 15)

# Contributions of rows to dimension 2
fviz_contrib(res.mca_pref_3, choice = "var", axes = 2, top = 15)

fviz_contrib(res.mca_posf_3, choice = "var", axes = 2, top = 15)

fviz_cos2(res.mca_posf_3, choice = "var", axes = 1:2)

head(round(var_posf_3$contrib,2), 49)
##                          Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_since_yng_br_0    0.98  1.81  0.28  0.85  2.22
## ea_read_since_yng_br_1    1.81  0.24  0.44  0.03  2.91
## ea_read_since_yng_br_2    0.14  1.05  0.15  1.71  0.00
## ea_read_since_yng_br_3    0.14  0.75  0.33  2.36  1.29
## ea_read_since_yng_br_4    0.72  2.97  2.87  1.64  0.43
## ea_read_since_yng_br_5    4.08  0.05  4.99  1.36  0.06
## ea_read_since_yng_br_6    4.66  4.53  1.63  3.01  0.05
## ea_indr_since_yng_br_0    1.24  1.51  0.51  1.25  4.70
## ea_indr_since_yng_br_1    1.91  0.72  0.80  0.00  4.00
## ea_indr_since_yng_br_2    0.11  1.49  0.25  2.19  0.05
## ea_indr_since_yng_br_3    0.26  0.96  0.14  1.05  1.52
## ea_indr_since_yng_br_4    0.66  2.57  1.99  1.68  0.81
## ea_indr_since_yng_br_5    3.92  0.20  3.49  1.26  0.01
## ea_indr_since_yng_br_6    2.42  2.71  1.91  3.53  0.06
## ea_letter_since_yng_br_0  1.59  2.47  0.70  1.97  3.69
## ea_letter_since_yng_br_1  1.53  0.19  1.24  0.31  6.99
## ea_letter_since_yng_br_2  0.00  1.74  0.55  2.19  0.23
## ea_letter_since_yng_br_3  0.42  1.53  0.11  2.63  2.96
## ea_letter_since_yng_br_4  1.77  3.49  4.03  2.97  1.54
## ea_letter_since_yng_br_5  5.64  0.34  6.57  2.06  0.05
## ea_letter_since_yng_br_6  4.79  4.30  1.41  2.86  0.37
## ea_tv_since_yng_br_0      2.66  3.38  0.37  2.94  6.63
## ea_tv_since_yng_br_1      2.76  0.83  1.39  0.01  8.28
## ea_tv_since_yng_br_2      0.61  0.77  0.93  2.68  0.01
## ea_tv_since_yng_br_3      0.02  1.21  0.43  5.40  3.89
## ea_tv_since_yng_br_4      0.87  5.49  4.68  2.42  0.59
## ea_tv_since_yng_br_5      6.13  0.10  6.27  2.79  0.08
## ea_tv_since_yng_br_6      3.62  5.83  3.78  2.12  0.30
## ea_boardg_since_yng_br_0  1.08  2.19  0.71  2.49  4.21
## ea_boardg_since_yng_br_1  1.83  0.74  0.38  0.02  4.29
## ea_boardg_since_yng_br_2  0.12  1.65  0.86  2.38  0.28
## ea_boardg_since_yng_br_3  0.58  2.01  0.06  0.78  1.03
## ea_boardg_since_yng_br_4  1.26  3.24  3.24  2.52  0.90
## ea_boardg_since_yng_br_5  7.23  0.52  7.66  2.06  0.02
## ea_boardg_since_yng_br_6  3.83  4.74  3.07  4.29  0.47
## ea_vidg_since_yng_br_0    2.38  2.88  0.23  3.02  6.17
## ea_vidg_since_yng_br_1    2.81  1.17  1.03  0.01  7.13
## ea_vidg_since_yng_br_2    0.53  0.64  1.44  3.99  0.03
## ea_vidg_since_yng_br_3    0.01  1.20  0.30  3.79  5.54
## ea_vidg_since_yng_br_4    0.25  5.74  4.81  1.95  0.54
## ea_vidg_since_yng_br_5    5.64  0.11  5.11  2.60  0.05
## ea_vidg_since_yng_br_6    2.96  5.20  3.77  1.15  0.21
## ea_edapp_since_yng_br_0   2.09  1.95  0.27  2.46  3.63
## ea_edapp_since_yng_br_1   2.37  0.91  1.05  0.05  6.55
## ea_edapp_since_yng_br_2   0.50  0.37  1.16  3.17  0.02
## ea_edapp_since_yng_br_3   0.14  1.15  0.31  3.87  4.39
## ea_edapp_since_yng_br_4   0.31  6.48  3.41  1.05  0.48
## ea_edapp_since_yng_br_5   5.52  0.07  5.33  1.96  0.01
## ea_edapp_since_yng_br_6   3.11  3.79  3.59  1.07  0.35

15 principais categorias de variáveis que contribuem para as dimensões:

# Contributions of rows to dimension 1
fviz_contrib(res.mca_posf_3, choice = "var", axes = 1, top = 15)

# Contributions of rows to dimension 2
fviz_contrib(res.mca_posf_3, choice = "var", axes = 2, top = 15)

fviz_contrib(res.mca_posf_3, choice = "var", axes = 1:2, top = 15)

fviz_mca_var(res.mca_posf_3, alpha.var="contrib",
             repel = TRUE,
             ggtheme = theme_minimal())
## Warning: ggrepel: 24 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

##Gráfico de indivíduos Resultados

lista contendo as coordenadas, o cos2 e as contribuições dos indivíduos:

ind_posf_3 <- get_mca_ind(res.mca_posf_3)
ind_posf_3
## Multiple Correspondence Analysis Results for individuals
##  ===================================================
##   Name       Description                       
## 1 "$coord"   "Coordinates for the individuals" 
## 2 "$cos2"    "Cos2 for the individuals"        
## 3 "$contrib" "contributions of the individuals"

colorir os indivíduos por seus valores de cos2:

fviz_mca_ind(res.mca_posf_3, col.ind = "cos2", 
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE, # Avoid text overlapping (slow if many points)
             ggtheme = theme_minimal())
## Warning: ggrepel: 531 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

# Cos2 of individuals
fviz_cos2(res.mca_posf_3, choice = "ind", axes = 1:2, top = 20)

# Contribution of individuals to the dimensions
fviz_contrib(res.mca_posf_3, choice = "ind", axes = 1:2, top = 20)

PANDEMIA COMPLETA

db_bloco_3f <- as.data.frame(lapply(db_bloco_3, as.factor))
summary(db_bloco_3f)
##  ea_read_b4_ALL_br ea_indr_b4_ALL_br ea_letter_b4_yng_br ea_tv_b4_yng_br
##  0:113             0:114             0:198               0:121          
##  1:181             1:163             1:188               1:137          
##  2:106             2:115             2:113               2:126          
##  3: 51             3: 64             3: 36               3: 55          
##  4:114             4:109             4: 57               4:111          
##  5: 39             5: 34             5: 18               5: 42          
##  6: 11             6: 16             6:  5               6: 23          
##  ea_boardg_b4_yng_br ea_vidg_b4_yng_br ea_edapp_b4_yng_br ea_read_since_yng_br
##  0:158               0:115             0:156              0:153               
##  1:208               1:150             1:132              1:156               
##  2:135               2:127             2:110              2: 91               
##  3: 55               3: 60             3: 69              3: 64               
##  4: 32               4: 97             4: 87              4: 98               
##  5: 16               5: 39             5: 32              5: 32               
##  6: 11               6: 27             6: 29              6: 21               
##  ea_indr_since_yng_br ea_letter_since_yng_br ea_tv_since_yng_br
##  0:121                0:206                  0: 90             
##  1:165                1:157                  1:121             
##  2: 86                2: 98                  2: 97             
##  3: 65                3: 55                  3: 77             
##  4:112                4: 71                  4:140             
##  5: 36                5: 14                  5: 43             
##  6: 30                6: 14                  6: 47             
##  ea_boardg_since_yng_br ea_vidg_since_yng_br ea_edapp_since_yng_br
##  0:126                  0: 70                0:147                
##  1:188                  1:115                1: 99                
##  2:144                  2: 99                2: 76                
##  3: 65                  3: 73                3: 62                
##  4: 56                  4:141                4:126                
##  5: 20                  5: 59                5: 53                
##  6: 16                  6: 58                6: 52

Podemos observar nos gráficos abaixo a frequência absoluta respostas para cada categoria de cada pergunta.

for (i in 1:14) {
  plot(db_bloco_3f[,i], main=colnames(db_bloco_3f)[i],
       ylab = "Count", col="steelblue", las = 2)
  }

MCA(db_bloco_3f,ncp = 5, graph = TRUE) 

## Warning: ggrepel: 3 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 14 variables
## *The results are available in the following objects:
## 
##    name              description                       
## 1  "$eig"            "eigenvalues"                     
## 2  "$var"            "results for the variables"       
## 3  "$var$coord"      "coord. of the categories"        
## 4  "$var$cos2"       "cos2 for the categories"         
## 5  "$var$contrib"    "contributions of the categories" 
## 6  "$var$v.test"     "v-test for the categories"       
## 7  "$ind"            "results for the individuals"     
## 8  "$ind$coord"      "coord. for the individuals"      
## 9  "$ind$cos2"       "cos2 for the individuals"        
## 10 "$ind$contrib"    "contributions of the individuals"
## 11 "$call"           "intermediate results"            
## 12 "$call$marge.col" "weights of columns"              
## 13 "$call$marge.li"  "weights of rows"
res.mca_3f <- MCA(db_bloco_3f, graph = FALSE)
print(res.mca_3f)
## **Results of the Multiple Correspondence Analysis (MCA)**
## The analysis was performed on 615 individuals, described by 14 variables
## *The results are available in the following objects:
## 
##    name              description                       
## 1  "$eig"            "eigenvalues"                     
## 2  "$var"            "results for the variables"       
## 3  "$var$coord"      "coord. of the categories"        
## 4  "$var$cos2"       "cos2 for the categories"         
## 5  "$var$contrib"    "contributions of the categories" 
## 6  "$var$v.test"     "v-test for the categories"       
## 7  "$ind"            "results for the individuals"     
## 8  "$ind$coord"      "coord. for the individuals"      
## 9  "$ind$cos2"       "cos2 for the individuals"        
## 10 "$ind$contrib"    "contributions of the individuals"
## 11 "$call"           "intermediate results"            
## 12 "$call$marge.col" "weights of columns"              
## 13 "$call$marge.li"  "weights of rows"

#Visualização e interpretação

Pode-se observar que juntando os blocos pré e pós pandemia, conseguiremos explicar mais de 50% da variância apenas com 15 dimensões.

eig.val_3f <- get_eigenvalue(res.mca_3f)
eig.val_3f
##         eigenvalue variance.percent cumulative.variance.percent
## Dim.1  0.486127819        8.1021303                     8.10213
## Dim.2  0.401096817        6.6849469                    14.78708
## Dim.3  0.326654859        5.4442477                    20.23132
## Dim.4  0.293692829        4.8948805                    25.12621
## Dim.5  0.270513692        4.5085615                    29.63477
## Dim.6  0.179836730        2.9972788                    32.63205
## Dim.7  0.165824887        2.7637481                    35.39579
## Dim.8  0.132285531        2.2047589                    37.60055
## Dim.9  0.126813795        2.1135633                    39.71412
## Dim.10 0.120100961        2.0016827                    41.71580
## Dim.11 0.115450286        1.9241714                    43.63997
## Dim.12 0.113702571        1.8950429                    45.53501
## Dim.13 0.108293113        1.8048852                    47.33990
## Dim.14 0.104269529        1.7378255                    49.07772
## Dim.15 0.102230520        1.7038420                    50.78157
## Dim.16 0.096601558        1.6100260                    52.39159
## Dim.17 0.094781970        1.5796995                    53.97129
## Dim.18 0.091700816        1.5283469                    55.49964
## Dim.19 0.089830289        1.4971715                    56.99681
## Dim.20 0.087475022        1.4579170                    58.45473
## Dim.21 0.084891864        1.4148644                    59.86959
## Dim.22 0.079099394        1.3183232                    61.18791
## Dim.23 0.077654710        1.2942452                    62.48216
## Dim.24 0.075879914        1.2646652                    63.74682
## Dim.25 0.075015944        1.2502657                    64.99709
## Dim.26 0.072455869        1.2075978                    66.20469
## Dim.27 0.071697915        1.1949652                    67.39965
## Dim.28 0.069532270        1.1588712                    68.55852
## Dim.29 0.066993323        1.1165554                    69.67508
## Dim.30 0.066108456        1.1018076                    70.77689
## Dim.31 0.063688460        1.0614743                    71.83836
## Dim.32 0.062816679        1.0469447                    72.88531
## Dim.33 0.061114223        1.0185704                    73.90388
## Dim.34 0.059571821        0.9928637                    74.89674
## Dim.35 0.058675494        0.9779249                    75.87467
## Dim.36 0.055755478        0.9292580                    76.80392
## Dim.37 0.055318768        0.9219795                    77.72590
## Dim.38 0.053727342        0.8954557                    78.62136
## Dim.39 0.051445529        0.8574255                    79.47878
## Dim.40 0.049988277        0.8331379                    80.31192
## Dim.41 0.048942947        0.8157158                    81.12764
## Dim.42 0.047794227        0.7965704                    81.92421
## Dim.43 0.046513928        0.7752321                    82.69944
## Dim.44 0.045542450        0.7590408                    83.45848
## Dim.45 0.043884725        0.7314121                    84.18989
## Dim.46 0.041747200        0.6957867                    84.88568
## Dim.47 0.041021425        0.6836904                    85.56937
## Dim.48 0.039447633        0.6574606                    86.22683
## Dim.49 0.038505160        0.6417527                    86.86858
## Dim.50 0.038284159        0.6380693                    87.50665
## Dim.51 0.036667704        0.6111284                    88.11778
## Dim.52 0.035516679        0.5919447                    88.70973
## Dim.53 0.034232561        0.5705427                    89.28027
## Dim.54 0.034067177        0.5677863                    89.84805
## Dim.55 0.033719370        0.5619895                    90.41004
## Dim.56 0.032451072        0.5408512                    90.95090
## Dim.57 0.030985855        0.5164309                    91.46733
## Dim.58 0.029861745        0.4976957                    91.96502
## Dim.59 0.029322050        0.4887008                    92.45372
## Dim.60 0.029003904        0.4833984                    92.93712
## Dim.61 0.027996325        0.4666054                    93.40373
## Dim.62 0.027727139        0.4621190                    93.86585
## Dim.63 0.025048957        0.4174826                    94.28333
## Dim.64 0.024675591        0.4112598                    94.69459
## Dim.65 0.024418996        0.4069833                    95.10157
## Dim.66 0.023853676        0.3975613                    95.49913
## Dim.67 0.022813588        0.3802265                    95.87936
## Dim.68 0.021309303        0.3551551                    96.23451
## Dim.69 0.019835157        0.3305860                    96.56510
## Dim.70 0.019154128        0.3192355                    96.88434
## Dim.71 0.018316042        0.3052674                    97.18960
## Dim.72 0.017884388        0.2980731                    97.48768
## Dim.73 0.017056871        0.2842812                    97.77196
## Dim.74 0.016371185        0.2728531                    98.04481
## Dim.75 0.015510924        0.2585154                    98.30333
## Dim.76 0.014944094        0.2490682                    98.55239
## Dim.77 0.014362895        0.2393816                    98.79178
## Dim.78 0.012924055        0.2154009                    99.00718
## Dim.79 0.012090393        0.2015065                    99.20868
## Dim.80 0.011101960        0.1850327                    99.39372
## Dim.81 0.010105151        0.1684192                    99.56214
## Dim.82 0.009712734        0.1618789                    99.72401
## Dim.83 0.008319556        0.1386593                    99.86267
## Dim.84 0.008239601        0.1373267                   100.00000

Porcentagens de inércia explicadas por cada dimensão MCA

fviz_screeplot(res.mca_3f, addlabels = TRUE, ylim = c(0, 10))

Biplot de indivíduos e categorias de variáveis: Os pontos azuis representam indivíduos e os pontos vermelhos representam as variáveis, quanto mais próximos uns dos outros mais similares são essas observações. Podemos observar um cluster de indivíduos que não são explicados pela dimensão 1 e nem pela dimensãso 2 no canto inferior esquerdo do gráfico.

fviz_mca_biplot(res.mca_3f, 
               repel = TRUE, # Avoid text overlapping (slow if many point)
               ggtheme = theme_minimal())
## Warning: ggrepel: 525 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 67 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

#Gráfico de variáveis #Resultados Usada para extrair os resultados para categorias de variáveis. Esta função retorna uma lista contendo as coordenadas, o cos2 e a contribuição das categorias de variáveis:

var_3f <- get_mca_var(res.mca_3f)
var_3f
## Multiple Correspondence Analysis Results for variables
##  ===================================================
##   Name       Description                  
## 1 "$coord"   "Coordinates for categories" 
## 2 "$cos2"    "Cos2 for categories"        
## 3 "$contrib" "contributions of categories"
# Coordinates
var_3f$coord
##                                Dim 1      Dim 2        Dim 3         Dim 4
## ea_read_b4_ALL_br_0      -0.34023445  0.6098388 -0.566058362  0.0013399019
## ea_read_b4_ALL_br_1      -0.32793404  0.2336533  0.284721667 -0.0213279464
## ea_read_b4_ALL_br_2      -0.21226636 -0.4560361  0.400784805  0.0822269791
## ea_read_b4_ALL_br_3      -0.07987968 -0.3566814  0.452358046 -0.0398479487
## ea_read_b4_ALL_br_4       0.27569655 -0.6721408 -0.565125401  0.0474921975
## ea_read_b4_ALL_br_5       1.92469051  0.4468995  0.153089199 -0.7482083129
## ea_read_b4_ALL_br_6       1.62584673  1.3202300  0.484576411  1.8901044242
## ea_indr_b4_ALL_br_0      -0.34400898  0.5556029 -0.810089589  0.0948216544
## ea_indr_b4_ALL_br_1      -0.47711642  0.2697326  0.484359728 -0.1747165407
## ea_indr_b4_ALL_br_2      -0.23413997 -0.2632049  0.378000616  0.1028707860
## ea_indr_b4_ALL_br_3       0.13291402 -0.6127583  0.304454689  0.1048244210
## ea_indr_b4_ALL_br_4       0.47445054 -0.6559181 -0.621653592 -0.0521056446
## ea_indr_b4_ALL_br_5       1.79719449  0.5153544  0.372308403 -0.2883380504
## ea_indr_b4_ALL_br_6       1.41167991  1.0095608  0.346635145  0.9133270736
## ea_letter_b4_yng_br_0    -0.37890639  0.4302642 -0.578087172 -0.0364202393
## ea_letter_b4_yng_br_1    -0.35527993  0.2384165  0.420939905 -0.0895948018
## ea_letter_b4_yng_br_2     0.01940297 -0.6306630  0.558557172  0.2232439968
## ea_letter_b4_yng_br_3     0.29218387 -1.0130107  0.135984375  0.0518375591
## ea_letter_b4_yng_br_4     0.96463080 -0.9813624 -0.868411542  0.1535683511
## ea_letter_b4_yng_br_5     3.44546114  1.4928609  0.619714955 -1.4286374584
## ea_letter_b4_yng_br_6     2.42053646  1.3569676  1.131349700  2.7848769183
## ea_tv_b4_yng_br_0        -0.57980860  0.6653764 -0.788107939 -0.1519870979
## ea_tv_b4_yng_br_1        -0.53847900  0.3734180  0.487395760 -0.2410335950
## ea_tv_b4_yng_br_2        -0.27733778 -0.4155502  0.582630238  0.1378019331
## ea_tv_b4_yng_br_3         0.07253817 -0.6772478  0.386914446  0.4496439949
## ea_tv_b4_yng_br_4         0.49237484 -0.8851662 -0.699380446  0.1430021734
## ea_tv_b4_yng_br_5         2.08203899  0.6474819  0.279533229 -1.5007580221
## ea_tv_b4_yng_br_6         1.42539909  1.2607980 -0.009262138  2.4555292931
## ea_boardg_b4_yng_br_0    -0.28503760  0.6479613 -0.604049646  0.0917366825
## ea_boardg_b4_yng_br_1    -0.34850159  0.1231110  0.262485108 -0.1977170478
## ea_boardg_b4_yng_br_2    -0.09044949 -0.5019738  0.466184530  0.0744795643
## ea_boardg_b4_yng_br_3     0.49759438 -0.9292323 -0.081075343  0.1324555020
## ea_boardg_b4_yng_br_4     0.69996504 -1.3801865 -0.992345754  0.3134511941
## ea_boardg_b4_yng_br_5     3.29639769  1.2571104  0.399312147 -1.8202901623
## ea_boardg_b4_yng_br_6     2.47509234  1.3583156  0.703022151  2.5804691537
## ea_vidg_b4_yng_br_0      -0.50941072  0.6058703 -0.776578804 -0.1725638859
## ea_vidg_b4_yng_br_1      -0.54437179  0.3720686  0.509958556 -0.1601312567
## ea_vidg_b4_yng_br_2      -0.26086417 -0.4588276  0.601567642  0.0571843346
## ea_vidg_b4_yng_br_3       0.29250714 -0.6158647  0.186510107  0.3573632430
## ea_vidg_b4_yng_br_4       0.37124869 -0.8831973 -0.891822547  0.0688606439
## ea_vidg_b4_yng_br_5       2.06142544  0.4374692  0.298107005 -1.6071821719
## ea_vidg_b4_yng_br_6       1.45965208  1.4202390  0.003840595  2.6355908351
## ea_edapp_b4_yng_br_0     -0.34745330  0.4624115 -0.590953398 -0.1111783123
## ea_edapp_b4_yng_br_1     -0.57276032  0.3362901  0.429722769 -0.1978575987
## ea_edapp_b4_yng_br_2     -0.23470652 -0.3656896  0.729419549  0.0175853228
## ea_edapp_b4_yng_br_3      0.11839714 -0.5080980  0.270842551  0.4242347111
## ea_edapp_b4_yng_br_4      0.41576561 -1.1017346 -0.837464159  0.0066314183
## ea_edapp_b4_yng_br_5      1.98852647  0.4758632  0.082218231 -1.5662779830
## ea_edapp_b4_yng_br_6      1.64313572  1.3579800  0.233428850  2.1309793529
## ea_read_since_yng_br_0   -0.35678948  0.4595136 -0.486136535  0.0355513424
## ea_read_since_yng_br_1   -0.45119985  0.2142820  0.212622730 -0.2114990452
## ea_read_since_yng_br_2   -0.26082213 -0.4274673  0.562021780  0.0928508065
## ea_read_since_yng_br_3    0.09865565 -0.4517482  0.547457530  0.2101608347
## ea_read_since_yng_br_4    0.41584825 -0.8192976 -0.685005854  0.1996363999
## ea_read_since_yng_br_5    1.70063976  0.2991336  0.200956881 -1.4966860473
## ea_read_since_yng_br_6    2.24872463  1.6569870  0.748973026  1.6183031035
## ea_indr_since_yng_br_0   -0.44249567  0.5752882 -0.720319014  0.1088063755
## ea_indr_since_yng_br_1   -0.42280819  0.3006164  0.317309122 -0.2062697394
## ea_indr_since_yng_br_2   -0.28990911 -0.5476669  0.662607080  0.1105708962
## ea_indr_since_yng_br_3    0.11552045 -0.5281234  0.401088749  0.1233349176
## ea_indr_since_yng_br_4    0.41003999 -0.7472877 -0.591352714  0.0001248723
## ea_indr_since_yng_br_5    1.51769088  0.4409849  0.071350297 -1.0379040151
## ea_indr_since_yng_br_6    1.33891107  1.0012189  0.513683709  1.3564542564
## ea_letter_since_yng_br_0 -0.39758235  0.5016895 -0.632853178  0.0579527848
## ea_letter_since_yng_br_1 -0.42793142  0.2018179  0.487394560 -0.2208596373
## ea_letter_since_yng_br_2 -0.01619833 -0.5829279  0.692864944  0.0604054509
## ea_letter_since_yng_br_3  0.17236407 -0.7499698  0.616340678  0.3021300799
## ea_letter_since_yng_br_4  0.84250910 -1.1107096 -0.925773281  0.1115444191
## ea_letter_since_yng_br_5  2.90594270  1.0859128  0.329432038 -2.6129498329
## ea_letter_since_yng_br_6  2.90666176  1.9285302  0.940368664  2.0615317638
## ea_tv_since_yng_br_0     -0.70295293  0.9039510 -1.023104598 -0.0652560921
## ea_tv_since_yng_br_1     -0.63905128  0.4397003  0.394390285 -0.3823352238
## ea_tv_since_yng_br_2     -0.40613416 -0.2978866  0.712130404 -0.0165952908
## ea_tv_since_yng_br_3     -0.13056739 -0.5541037  0.762471260  0.3560082491
## ea_tv_since_yng_br_4      0.31129238 -0.9411542 -0.669791253  0.1827638355
## ea_tv_since_yng_br_5      1.78944679  0.2210306  0.017073821 -1.7710965332
## ea_tv_since_yng_br_6      1.47898992  1.2608285  0.204422429  1.6362326030
## ea_boardg_since_yng_br_0 -0.37483318  0.6322085 -0.846850632  0.0596122332
## ea_boardg_since_yng_br_1 -0.38832915  0.3055777  0.239486191 -0.1209616199
## ea_boardg_since_yng_br_2 -0.21861822 -0.4232069  0.531652605 -0.0415933357
## ea_boardg_since_yng_br_3  0.24918789 -0.8375312  0.226195377  0.3165769526
## ea_boardg_since_yng_br_4  0.73932756 -1.2577366 -0.932503429  0.1020314447
## ea_boardg_since_yng_br_5  2.87677359  1.0027223  0.461168988 -2.4981417259
## ea_boardg_since_yng_br_6  2.28630343  1.7908281  0.838494588  2.8056659499
## ea_vidg_since_yng_br_0   -0.74981327  0.9747859 -1.056527295 -0.1652060972
## ea_vidg_since_yng_br_1   -0.64058285  0.5122671  0.359225431 -0.2975519965
## ea_vidg_since_yng_br_2   -0.35580711 -0.2790884  0.780130831 -0.0275065078
## ea_vidg_since_yng_br_3   -0.13787208 -0.5353303  0.620976893  0.2676388676
## ea_vidg_since_yng_br_4    0.16315462 -0.9396854 -0.671506068  0.1976860276
## ea_vidg_since_yng_br_5    1.36644287  0.2296450 -0.035622291 -1.3406781143
## ea_vidg_since_yng_br_6    1.16928606  1.0087880  0.118375712  1.3826671067
## ea_edapp_since_yng_br_0  -0.49390368  0.5562376 -0.673176358 -0.1091843023
## ea_edapp_since_yng_br_1  -0.59242450  0.4553445  0.466753894 -0.2484666531
## ea_edapp_since_yng_br_2  -0.43724701 -0.1945980  0.800621510 -0.0065314499
## ea_edapp_since_yng_br_3   0.03978740 -0.5781787  0.707174640  0.3267230215
## ea_edapp_since_yng_br_4   0.14803600 -1.0641685 -0.520711222  0.1670006086
## ea_edapp_since_yng_br_5   1.44752499  0.1622542  0.082560471 -1.3530915593
## ea_edapp_since_yng_br_6   1.28166268  0.9476205  0.178656454  1.3761467291
##                                 Dim 5
## ea_read_b4_ALL_br_0       0.336852495
## ea_read_b4_ALL_br_1      -0.488053241
## ea_read_b4_ALL_br_2       0.357079718
## ea_read_b4_ALL_br_3       0.572953239
## ea_read_b4_ALL_br_4      -0.215208994
## ea_read_b4_ALL_br_5       0.248839262
## ea_read_b4_ALL_br_6      -0.178969683
## ea_indr_b4_ALL_br_0       0.514293180
## ea_indr_b4_ALL_br_1      -0.604402426
## ea_indr_b4_ALL_br_2       0.474875984
## ea_indr_b4_ALL_br_3       0.475172029
## ea_indr_b4_ALL_br_4      -0.474246407
## ea_indr_b4_ALL_br_5       0.069966726
## ea_indr_b4_ALL_br_6       0.261275906
## ea_letter_b4_yng_br_0     0.328305197
## ea_letter_b4_yng_br_1    -0.622533322
## ea_letter_b4_yng_br_2     0.633848637
## ea_letter_b4_yng_br_3     0.498435816
## ea_letter_b4_yng_br_4    -0.734889767
## ea_letter_b4_yng_br_5     0.347663698
## ea_letter_b4_yng_br_6    -0.381195976
## ea_tv_b4_yng_br_0         0.552954493
## ea_tv_b4_yng_br_1        -0.737294035
## ea_tv_b4_yng_br_2         0.478278727
## ea_tv_b4_yng_br_3         0.688396187
## ea_tv_b4_yng_br_4        -0.526314583
## ea_tv_b4_yng_br_5         0.049369120
## ea_tv_b4_yng_br_6        -0.333726314
## ea_boardg_b4_yng_br_0     0.354020604
## ea_boardg_b4_yng_br_1    -0.425655089
## ea_boardg_b4_yng_br_2     0.415833948
## ea_boardg_b4_yng_br_3     0.060673071
## ea_boardg_b4_yng_br_4    -0.765638490
## ea_boardg_b4_yng_br_5     0.169199528
## ea_boardg_b4_yng_br_6    -0.461850879
## ea_vidg_b4_yng_br_0       0.618221507
## ea_vidg_b4_yng_br_1      -0.684664768
## ea_vidg_b4_yng_br_2       0.484276230
## ea_vidg_b4_yng_br_3       0.550470106
## ea_vidg_b4_yng_br_4      -0.603356134
## ea_vidg_b4_yng_br_5       0.124102199
## ea_vidg_b4_yng_br_6      -0.342277275
## ea_edapp_b4_yng_br_0      0.377836267
## ea_edapp_b4_yng_br_1     -0.647216642
## ea_edapp_b4_yng_br_2      0.479276959
## ea_edapp_b4_yng_br_3      0.517913608
## ea_edapp_b4_yng_br_4     -0.554460177
## ea_edapp_b4_yng_br_5      0.009742579
## ea_edapp_b4_yng_br_6     -0.484141119
## ea_read_since_yng_br_0    0.225845664
## ea_read_since_yng_br_1   -0.556390759
## ea_read_since_yng_br_2    0.334379294
## ea_read_since_yng_br_3    0.648693640
## ea_read_since_yng_br_4   -0.356459919
## ea_read_since_yng_br_5    0.533455348
## ea_read_since_yng_br_6   -0.087611234
## ea_indr_since_yng_br_0    0.540572365
## ea_indr_since_yng_br_1   -0.601796204
## ea_indr_since_yng_br_2    0.429086735
## ea_indr_since_yng_br_3    0.494024172
## ea_indr_since_yng_br_4   -0.418235682
## ea_indr_since_yng_br_5    0.298371592
## ea_indr_since_yng_br_6    0.032503541
## ea_letter_since_yng_br_0  0.283509091
## ea_letter_since_yng_br_1 -0.685230657
## ea_letter_since_yng_br_2  0.493921670
## ea_letter_since_yng_br_3  0.895173705
## ea_letter_since_yng_br_4 -0.746084013
## ea_letter_since_yng_br_5  0.649844295
## ea_letter_since_yng_br_6 -0.327599442
## ea_tv_since_yng_br_0      0.655692258
## ea_tv_since_yng_br_1     -0.906061237
## ea_tv_since_yng_br_2      0.354082006
## ea_tv_since_yng_br_3      0.869480192
## ea_tv_since_yng_br_4     -0.397875032
## ea_tv_since_yng_br_5      0.368099326
## ea_tv_since_yng_br_6     -0.229799773
## ea_boardg_since_yng_br_0  0.383584754
## ea_boardg_since_yng_br_1 -0.593701091
## ea_boardg_since_yng_br_2  0.439894159
## ea_boardg_since_yng_br_3  0.393876211
## ea_boardg_since_yng_br_4 -0.563343343
## ea_boardg_since_yng_br_5  0.642204852
## ea_boardg_since_yng_br_6 -0.434966023
## ea_vidg_since_yng_br_0    0.750241068
## ea_vidg_since_yng_br_1   -0.842954422
## ea_vidg_since_yng_br_2    0.337307240
## ea_vidg_since_yng_br_3    0.823723037
## ea_vidg_since_yng_br_4   -0.377467329
## ea_vidg_since_yng_br_5    0.282445510
## ea_vidg_since_yng_br_6   -0.216270799
## ea_edapp_since_yng_br_0   0.379946956
## ea_edapp_since_yng_br_1  -0.860455455
## ea_edapp_since_yng_br_2   0.349882333
## ea_edapp_since_yng_br_3   0.831054411
## ea_edapp_since_yng_br_4  -0.359594682
## ea_edapp_since_yng_br_5   0.190167488
## ea_edapp_since_yng_br_6  -0.260644040
var_3f$cos2
##                                 Dim 1       Dim 2        Dim 3        Dim 4
## ea_read_b4_ALL_br_0      2.605741e-02 0.083715289 7.212688e-02 4.041297e-07
## ea_read_b4_ALL_br_1      4.484994e-02 0.022768400 3.380881e-02 1.897086e-04
## ea_read_b4_ALL_br_2      9.383188e-03 0.043309832 3.345111e-02 1.408046e-03
## ea_read_b4_ALL_br_3      5.769839e-04 0.011504085 1.850358e-02 1.435830e-04
## ea_read_b4_ALL_br_4      1.729537e-02 0.102798700 7.267027e-02 5.132296e-04
## ea_read_b4_ALL_br_5      2.508210e-01 0.013522649 1.586833e-03 3.790419e-02
## ea_read_b4_ALL_br_6      4.814098e-02 0.031743510 4.276419e-03 6.506199e-02
## ea_indr_b4_ALL_br_0      2.692816e-02 0.070241882 1.493252e-01 2.045890e-03
## ea_indr_b4_ALL_br_1      8.209145e-02 0.026237111 8.460289e-02 1.100822e-02
## ea_indr_b4_ALL_br_2      1.260895e-02 0.015933669 3.286343e-02 2.433952e-03
## ea_indr_b4_ALL_br_3      2.051965e-03 0.043612076 1.076648e-02 1.276302e-03
## ea_indr_b4_ALL_br_4      4.849064e-02 0.092677689 8.324782e-02 5.848514e-04
## ea_indr_b4_ALL_br_5      1.890136e-01 0.015542281 8.111636e-03 4.865267e-03
## ea_indr_b4_ALL_br_6      5.323112e-02 0.027224389 3.209507e-03 2.228157e-02
## ea_letter_b4_yng_br_0    6.816995e-02 0.087902178 1.586777e-01 6.298175e-04
## ea_letter_b4_yng_br_1    5.557396e-02 0.025026646 7.801357e-02 3.534236e-03
## ea_letter_b4_yng_br_2    8.474440e-05 0.089530176 7.022795e-02 1.121849e-02
## ea_letter_b4_yng_br_3    5.308067e-03 0.063804598 1.149746e-03 1.670756e-04
## ea_letter_b4_yng_br_4    9.505236e-02 0.098378331 7.703566e-02 2.409040e-03
## ea_letter_b4_yng_br_5    3.579257e-01 0.067194984 1.157930e-02 6.153784e-02
## ea_letter_b4_yng_br_6    4.802456e-02 0.015093124 1.049141e-02 6.357000e-02
## ea_tv_b4_yng_br_0        8.234320e-02 0.108440921 1.521352e-01 5.658116e-03
## ea_tv_b4_yng_br_1        8.310559e-02 0.039965316 6.808574e-02 1.665129e-02
## ea_tv_b4_yng_br_2        1.981891e-02 0.044494750 8.746770e-02 4.892967e-03
## ea_tv_b4_yng_br_3        5.167826e-04 0.045047416 1.470295e-02 1.985694e-02
## ea_tv_b4_yng_br_4        5.339298e-02 0.172560778 1.077257e-01 4.503786e-03
## ea_tv_b4_yng_br_5        3.177404e-01 0.030729107 5.727453e-03 1.650882e-01
## ea_tv_b4_yng_br_6        7.893672e-02 0.061758562 3.332949e-06 2.342590e-01
## ea_boardg_b4_yng_br_0    2.808958e-02 0.145157368 1.261497e-01 2.909558e-03
## ea_boardg_b4_yng_br_1    6.206953e-02 0.007745731 3.521099e-02 1.997824e-02
## ea_boardg_b4_yng_br_2    2.300937e-03 0.070868733 6.112350e-02 1.560152e-03
## ea_boardg_b4_yng_br_3    2.431787e-02 0.084805357 6.455832e-04 1.723117e-03
## ea_boardg_b4_yng_br_4    2.689268e-02 0.104557927 5.405146e-02 5.392887e-03
## ea_boardg_b4_yng_br_5    2.902501e-01 0.042212398 4.259104e-03 8.850634e-02
## ea_boardg_b4_yng_br_6    1.115677e-01 0.033601378 9.001062e-03 1.212699e-01
## ea_vidg_b4_yng_br_0      5.968483e-02 0.084428124 1.387072e-01 6.849008e-03
## ea_vidg_b4_yng_br_1      9.559376e-02 0.044656469 8.388959e-02 8.271619e-03
## ea_vidg_b4_yng_br_2      1.770976e-02 0.054787682 9.417873e-02 8.510166e-04
## ea_vidg_b4_yng_br_3      9.249776e-03 0.041004257 3.760651e-03 1.380632e-02
## ea_vidg_b4_yng_br_4      2.580904e-02 0.146068789 1.489357e-01 8.879410e-04
## ea_vidg_b4_yng_br_5      2.877249e-01 0.012957971 6.017090e-03 1.748930e-01
## ea_vidg_b4_yng_br_6      9.783295e-02 0.092620960 6.773038e-07 3.189645e-01
## ea_edapp_b4_yng_br_0     4.103031e-02 0.072672347 1.186912e-01 4.200994e-03
## ea_edapp_b4_yng_br_1     8.965461e-02 0.030906867 5.046654e-02 1.069873e-02
## ea_edapp_b4_yng_br_2     1.199918e-02 0.029129070 1.158927e-01 6.735999e-05
## ea_edapp_b4_yng_br_3     1.771491e-03 0.032625073 9.270224e-03 2.274410e-02
## ea_edapp_b4_yng_br_4     2.848279e-02 0.200004280 1.155627e-01 7.245998e-06
## ea_edapp_b4_yng_br_5     2.170422e-01 0.012429273 3.710374e-04 1.346540e-01
## ea_edapp_b4_yng_br_6     1.336126e-01 0.091261400 2.696556e-03 2.247289e-01
## ea_read_since_yng_br_0   4.215737e-02 0.069927217 7.826471e-02 4.185636e-04
## ea_read_since_yng_br_1   6.919103e-02 0.015605695 1.536496e-02 1.520298e-02
## ea_read_since_yng_br_2   1.181405e-02 0.031733343 5.485502e-02 1.497206e-03
## ea_read_since_yng_br_3   1.130504e-03 0.023703982 3.481202e-02 5.130172e-03
## ea_read_since_yng_br_4   3.277972e-02 0.127238609 8.894552e-02 7.554661e-03
## ea_read_since_yng_br_5   1.587472e-01 0.004911475 2.216599e-03 1.229541e-01
## ea_read_since_yng_br_6   1.787744e-01 0.097066880 1.983194e-02 9.258755e-02
## ea_indr_since_yng_br_0   4.795970e-02 0.081064251 1.270891e-01 2.899794e-03
## ea_indr_since_yng_br_1   6.554782e-02 0.033135741 3.691786e-02 1.560064e-02
## ea_indr_since_yng_br_2   1.366364e-02 0.048761350 7.137645e-02 1.987579e-03
## ea_indr_since_yng_br_3   1.577133e-03 0.032962605 1.901217e-02 1.797723e-03
## ea_indr_since_yng_br_4   3.743712e-02 0.124344258 7.786517e-02 3.472020e-09
## ea_indr_since_yng_br_5   1.432157e-01 0.012091254 3.165305e-04 6.697895e-02
## ea_indr_since_yng_br_6   9.193245e-02 0.051407146 1.353184e-02 9.435734e-02
## ea_letter_since_yng_br_0 7.961559e-02 0.126769259 2.017204e-01 1.691580e-03
## ea_letter_since_yng_br_1 6.277439e-02 0.013962193 8.143208e-02 1.672118e-02
## ea_letter_since_yng_br_2 4.973656e-05 0.064411765 9.099818e-02 6.916522e-04
## ea_letter_since_yng_br_3 2.917885e-03 0.055241083 3.730923e-02 8.965254e-03
## ea_letter_since_yng_br_4 9.264215e-02 0.161012849 1.118584e-01 1.623885e-03
## ea_letter_since_yng_br_5 1.967106e-01 0.027469041 2.528047e-03 1.590434e-01
## ea_letter_since_yng_br_6 1.968079e-01 0.086637609 2.059918e-02 9.899964e-02
## ea_tv_since_yng_br_0     8.471020e-02 0.140078978 1.794417e-01 7.300042e-04
## ea_tv_since_yng_br_1     1.000299e-01 0.047355659 3.809876e-02 3.580528e-02
## ea_tv_since_yng_br_2     3.088738e-02 0.016616667 9.496444e-02 5.157173e-05
## ea_tv_since_yng_br_3     2.439933e-03 0.043943079 8.320615e-02 1.813964e-02
## ea_tv_since_yng_br_4     2.856087e-02 0.261069433 1.322249e-01 9.844983e-03
## ea_tv_since_yng_br_5     2.407188e-01 0.003672631 2.191462e-05 2.358071e-01
## ea_tv_since_yng_br_6     1.810006e-01 0.131541117 3.457854e-03 2.215336e-01
## ea_boardg_since_yng_br_0 3.620243e-02 0.102986985 1.847887e-01 9.156563e-04
## ea_boardg_since_yng_br_1 6.639417e-02 0.041112446 2.525172e-02 6.442066e-03
## ea_boardg_since_yng_br_2 1.461216e-02 0.054757939 8.641666e-02 5.289189e-04
## ea_boardg_since_yng_br_3 7.338454e-03 0.082899639 6.046696e-03 1.184430e-02
## ea_boardg_since_yng_br_4 5.475831e-02 0.158473135 8.711182e-02 1.042904e-03
## ea_boardg_since_yng_br_5 2.781790e-01 0.033796704 7.148801e-03 2.097718e-01
## ea_boardg_since_yng_br_6 1.396243e-01 0.085664511 1.877992e-02 2.102641e-01
## ea_vidg_since_yng_br_0   7.221174e-02 0.122045017 1.433716e-01 3.505530e-03
## ea_vidg_since_yng_br_1   9.437967e-02 0.060356052 2.967987e-02 2.036355e-02
## ea_vidg_since_yng_br_2   2.428929e-02 0.014944071 1.167671e-01 1.451632e-04
## ea_vidg_since_yng_br_3   2.560214e-03 0.038598214 5.193671e-02 9.647659e-03
## ea_vidg_since_yng_br_4   7.918438e-03 0.262667118 1.341345e-01 1.162499e-02
## ea_vidg_since_yng_br_5   1.981345e-01 0.005596174 1.346545e-04 1.907332e-01
## ea_vidg_since_yng_br_6   1.423686e-01 0.105967487 1.459144e-03 1.990710e-01
## ea_edapp_since_yng_br_0  7.662244e-02 0.097183401 1.423407e-01 3.744483e-03
## ea_edapp_since_yng_br_1  6.733665e-02 0.039780086 4.179857e-02 1.184464e-02
## ea_edapp_since_yng_br_2  2.695743e-02 0.005339515 9.038146e-02 6.015116e-06
## ea_edapp_since_yng_br_3  1.774833e-04 0.037479240 5.606863e-02 1.196812e-02
## ea_edapp_since_yng_br_4  5.646722e-03 0.291798132 6.986434e-02 7.186175e-03
## ea_edapp_since_yng_br_5  1.976022e-01 0.002482742 6.428119e-04 1.726609e-01
## ea_edapp_since_yng_br_6  1.517199e-01 0.082939964 2.948033e-03 1.749139e-01
##                                 Dim 5
## ea_read_b4_ALL_br_0      2.554196e-02
## ea_read_b4_ALL_br_1      9.933979e-02
## ea_read_b4_ALL_br_2      2.655330e-02
## ea_read_b4_ALL_br_3      2.968448e-02
## ea_read_b4_ALL_br_4      1.053872e-02
## ea_read_b4_ALL_br_5      4.192566e-03
## ea_read_b4_ALL_br_6      5.833305e-04
## ea_indr_b4_ALL_br_0      6.018505e-02
## ea_indr_b4_ALL_br_1      1.317351e-01
## ea_indr_b4_ALL_br_2      5.186666e-02
## ea_indr_b4_ALL_br_3      2.622588e-02
## ea_indr_b4_ALL_br_4      4.844892e-02
## ea_indr_b4_ALL_br_5      2.864744e-04
## ea_indr_b4_ALL_br_6      1.823442e-03
## ea_letter_b4_yng_br_0    5.117816e-02
## ea_letter_b4_yng_br_1    1.706299e-01
## ea_letter_b4_yng_br_2    9.043694e-02
## ea_letter_b4_yng_br_3    1.544694e-02
## ea_letter_b4_yng_br_4    5.516772e-02
## ea_letter_b4_yng_br_5    3.644323e-03
## ea_letter_b4_yng_br_6    1.191069e-03
## ea_tv_b4_yng_br_0        7.489231e-02
## ea_tv_b4_yng_br_1        1.558024e-01
## ea_tv_b4_yng_br_2        5.894186e-02
## ea_tv_b4_yng_br_3        4.654270e-02
## ea_tv_b4_yng_br_4        6.100750e-02
## ea_tv_b4_yng_br_5        1.786510e-04
## ea_tv_b4_yng_br_6        4.327001e-03
## ea_boardg_b4_yng_br_0    4.333093e-02
## ea_boardg_b4_yng_br_1    9.259437e-02
## ea_boardg_b4_yng_br_2    4.863315e-02
## ea_boardg_b4_yng_br_3    3.615485e-04
## ea_boardg_b4_yng_br_4    3.217577e-02
## ea_boardg_b4_yng_br_5    7.647006e-04
## ea_boardg_b4_yng_br_6    3.884716e-03
## ea_vidg_b4_yng_br_0      8.790550e-02
## ea_vidg_b4_yng_br_1      1.512148e-01
## ea_vidg_b4_yng_br_2      6.103377e-02
## ea_vidg_b4_yng_br_3      3.275863e-02
## ea_vidg_b4_yng_br_4      6.816939e-02
## ea_vidg_b4_yng_br_5      1.042800e-03
## ea_vidg_b4_yng_br_6      5.379508e-03
## ea_edapp_b4_yng_br_0     4.851982e-02
## ea_edapp_b4_yng_br_1     1.144791e-01
## ea_edapp_b4_yng_br_2     5.003506e-02
## ea_edapp_b4_yng_br_3     3.389777e-02
## ea_edapp_b4_yng_br_4     5.065543e-02
## ea_edapp_b4_yng_br_5     5.209899e-06
## ea_edapp_b4_yng_br_6     1.159963e-02
## ea_read_since_yng_br_0   1.689168e-02
## ea_read_since_yng_br_1   1.052136e-01
## ea_read_since_yng_br_2   1.941730e-02
## ea_read_since_yng_br_3   4.887735e-02
## ea_read_since_yng_br_4   2.408557e-02
## ea_read_since_yng_br_5   1.561988e-02
## ea_read_since_yng_br_6   2.713641e-04
## ea_indr_since_yng_br_0   7.157578e-02
## ea_indr_since_yng_br_1   1.327915e-01
## ea_indr_since_yng_br_2   2.993181e-02
## ea_indr_since_yng_br_3   2.884344e-02
## ea_indr_since_yng_br_4   3.894863e-02
## ea_indr_since_yng_br_5   5.535271e-03
## ea_indr_since_yng_br_6   5.417847e-05
## ea_letter_since_yng_br_0 4.048348e-02
## ea_letter_since_yng_br_1 1.609562e-01
## ea_letter_since_yng_br_2 4.624361e-02
## ea_letter_since_yng_br_3 7.870264e-02
## ea_letter_since_yng_br_4 7.264988e-02
## ea_letter_since_yng_br_5 9.837216e-03
## ea_letter_since_yng_br_6 2.499999e-03
## ea_tv_since_yng_br_0     7.370269e-02
## ea_tv_since_yng_br_1     2.010822e-01
## ea_tv_since_yng_br_2     2.347738e-02
## ea_tv_since_yng_br_3     1.082001e-01
## ea_tv_since_yng_br_4     4.665818e-02
## ea_tv_since_yng_br_5     1.018597e-02
## ea_tv_since_yng_br_6     4.369671e-03
## ea_boardg_since_yng_br_0 3.791267e-02
## ea_boardg_since_yng_br_1 1.551907e-01
## ea_boardg_since_yng_br_2 5.916134e-02
## ea_boardg_since_yng_br_3 1.833455e-02
## ea_boardg_since_yng_br_4 3.179234e-02
## ea_boardg_since_yng_br_5 1.386309e-02
## ea_boardg_since_yng_br_6 5.053634e-03
## ea_vidg_since_yng_br_0   7.229416e-02
## ea_vidg_since_yng_br_1   1.634316e-01
## ea_vidg_since_yng_br_2   2.182915e-02
## ea_vidg_since_yng_br_3   9.138733e-02
## ea_vidg_since_yng_br_4   4.238376e-02
## ea_vidg_since_yng_br_5   8.465382e-03
## ea_vidg_since_yng_br_6   4.870444e-03
## ea_edapp_since_yng_br_0  4.534375e-02
## ea_edapp_since_yng_br_1  1.420503e-01
## ea_edapp_since_yng_br_2  1.726112e-02
## ea_edapp_since_yng_br_3  7.743289e-02
## ea_edapp_since_yng_br_4  3.331871e-02
## ea_edapp_since_yng_br_5  3.410453e-03
## ea_edapp_since_yng_br_6  6.274665e-03
var_3f$contrib
##                                 Dim 1      Dim 2        Dim 3        Dim 4
## ea_read_b4_ALL_br_0      0.3125225639 1.21690347 1.287385e+00 8.022837e-06
## ea_read_b4_ALL_br_1      0.4650481803 0.28613426 5.217078e-01 3.255968e-03
## ea_read_b4_ALL_br_2      0.1141075992 0.63833979 6.053912e-01 2.834249e-02
## ea_read_b4_ALL_br_3      0.0077747995 0.18787926 3.710586e-01 3.202474e-03
## ea_read_b4_ALL_br_4      0.2070207612 1.49132734 1.294501e+00 1.016841e-02
## ea_read_b4_ALL_br_5      3.4516980148 0.22554432 3.249836e-02 8.634022e-01
## ea_read_b4_ALL_br_6      0.6947015441 0.55518730 9.183854e-02 1.554060e+00
## ea_indr_b4_ALL_br_0      0.3223226157 1.01901716 2.659982e+00 4.053437e-02
## ea_indr_b4_ALL_br_1      0.8865081103 0.34340141 1.359663e+00 1.967701e-01
## ea_indr_b4_ALL_br_2      0.1506244068 0.23069247 5.842390e-01 4.812663e-02
## ea_indr_b4_ALL_br_3      0.0270126965 0.69583455 2.109274e-01 2.781048e-02
## ea_indr_b4_ALL_br_4      0.5862120901 1.35791737 1.497722e+00 1.170306e-02
## ea_indr_b4_ALL_br_5      2.6237076758 0.26148003 1.675685e-01 1.117857e-01
## ea_indr_b4_ALL_br_6      0.7617962433 0.47220718 6.835541e-02 5.278082e-01
## ea_letter_b4_yng_br_0    0.6791651963 1.06141036 2.352662e+00 1.038614e-02
## ea_letter_b4_yng_br_1    0.5669510720 0.30944122 1.184420e+00 5.967975e-02
## ea_letter_b4_yng_br_2    0.0010163917 1.30142993 1.253492e+00 2.227109e-01
## ea_letter_b4_yng_br_3    0.0734279398 1.06973984 2.366945e-02 3.825561e-03
## ea_letter_b4_yng_br_4    1.2671950701 1.58957595 1.528388e+00 5.315961e-02
## ea_letter_b4_yng_br_5    5.1052097651 1.16160593 2.457900e-01 1.452848e+00
## ea_letter_b4_yng_br_6    0.6999058701 0.26659783 2.275472e-01 1.533506e+00
## ea_tv_b4_yng_br_0        0.9718540374 1.55119994 2.672173e+00 1.105356e-01
## ea_tv_b4_yng_br_1        0.9490836301 0.55317020 1.157156e+00 3.147598e-01
## ea_tv_b4_yng_br_2        0.2315447740 0.63003586 1.520773e+00 9.462038e-02
## ea_tv_b4_yng_br_3        0.0069142060 0.73047531 2.927523e-01 4.397479e-01
## ea_tv_b4_yng_br_4        0.6429260920 2.51837419 1.930446e+00 8.976598e-02
## ea_tv_b4_yng_br_5        4.3498409924 0.50986148 1.166874e-01 3.740883e+00
## ea_tv_b4_yng_br_6        1.1164702686 1.05868441 7.015491e-05 5.484305e+00
## ea_boardg_b4_yng_br_0    0.3066957487 1.92089187 2.049793e+00 5.258316e-02
## ea_boardg_b4_yng_br_1    0.6035582997 0.09128605 5.095425e-01 3.215549e-01
## ea_boardg_b4_yng_br_2    0.0263871430 0.98501657 1.043175e+00 2.961499e-02
## ea_boardg_b4_yng_br_3    0.3253568811 1.37517809 1.285429e-02 3.815981e-02
## ea_boardg_b4_yng_br_4    0.3745838550 1.76511560 1.120427e+00 1.243350e-01
## ea_boardg_b4_yng_br_5    4.1537997877 0.73217430 9.070950e-02 2.096548e+00
## ea_boardg_b4_yng_br_6    1.6099851496 0.58768102 1.933029e-01 2.896633e+00
## ea_vidg_b4_yng_br_0      0.7129849608 1.22237589 2.465907e+00 1.354257e-01
## ea_vidg_b4_yng_br_1      1.0620106085 0.60129137 1.386975e+00 1.521062e-01
## ea_vidg_b4_yng_br_2      0.2064804562 0.77419504 1.634107e+00 1.642334e-02
## ea_vidg_b4_yng_br_3      0.1226508543 0.65897595 7.421022e-02 3.030222e-01
## ea_vidg_b4_yng_br_4      0.3194096054 2.19096188 2.743067e+00 1.818936e-02
## ea_vidg_b4_yng_br_5      3.9595537705 0.21612605 1.232301e-01 3.983807e+00
## ea_vidg_b4_yng_br_6      1.3743863667 1.57700925 1.416017e-05 7.416914e+00
## ea_edapp_b4_yng_br_0     0.4499495846 0.96589408 1.937041e+00 7.625499e-02
## ea_edapp_b4_yng_br_1     1.0345849279 0.43226468 8.666792e-01 2.043539e-01
## ea_edapp_b4_yng_br_2     0.1447735988 0.42595687 2.080919e+00 1.345231e-03
## ea_edapp_b4_yng_br_3     0.0231088271 0.51581238 1.799661e-01 4.910944e-01
## ea_edapp_b4_yng_br_4     0.3593045803 3.05788286 2.169499e+00 1.512988e-04
## ea_edapp_b4_yng_br_5     3.0231459009 0.20982725 7.691197e-03 3.104496e+00
## ea_edapp_b4_yng_br_6     1.8706444420 1.54857548 5.618428e-02 5.207868e+00
## ea_read_since_yng_br_0   0.4653308809 0.93548290 1.285629e+00 7.647279e-03
## ea_read_since_yng_br_1   0.7587677384 0.20741656 2.507562e-01 2.759592e-01
## ea_read_since_yng_br_2   0.1479028845 0.48149828 1.022011e+00 3.102532e-02
## ea_read_since_yng_br_3   0.0148823063 0.37819914 6.820066e-01 1.117859e-01
## ea_read_since_yng_br_4   0.4048948078 1.90483380 1.635017e+00 1.544575e-01
## ea_read_since_yng_br_5   2.2111642875 0.08291405 4.594767e-02 2.834751e+00
## ea_read_since_yng_br_6   2.5371037147 1.66957199 4.188508e-01 2.174916e+00
## ea_indr_since_yng_br_0   0.5660434867 1.15958866 2.232250e+00 5.664969e-02
## ea_indr_since_yng_br_1   0.7047208303 0.43177434 5.906861e-01 2.776249e-01
## ea_indr_since_yng_br_2   0.1726905443 0.74692850 1.342512e+00 4.157992e-02
## ea_indr_since_yng_br_3   0.0207241874 0.52496680 3.717937e-01 3.910110e-02
## ea_indr_since_yng_br_4   0.4499008540 1.81109551 1.392576e+00 6.906432e-08
## ea_indr_since_yng_br_5   1.9811416315 0.20272044 6.516311e-03 1.533629e+00
## ea_indr_since_yng_br_6   1.2849052222 0.87081720 2.814625e-01 2.182906e+00
## ea_letter_since_yng_br_0 0.7779788350 1.50136018 2.933465e+00 2.736020e-02
## ea_letter_since_yng_br_1 0.6869013525 0.18516834 1.326077e+00 3.028558e-01
## ea_letter_since_yng_br_2 0.0006143455 0.96428047 1.672750e+00 1.414106e-02
## ea_letter_since_yng_br_3 0.0390393464 0.89577274 7.428687e-01 1.985428e-01
## ea_letter_since_yng_br_4 1.2040760563 2.53633765 2.163592e+00 3.493474e-02
## ea_letter_since_yng_br_5 2.8245450224 0.47804150 5.402160e-02 3.780016e+00
## ea_letter_since_yng_br_6 2.8259430480 1.50774731 4.401818e-01 2.352944e+00
## ea_tv_since_yng_br_0     1.0625304361 2.12951107 3.349580e+00 1.515612e-02
## ea_tv_since_yng_br_1     1.1806010319 0.67740199 6.691839e-01 6.994835e-01
## ea_tv_since_yng_br_2     0.3822584959 0.24924205 1.749035e+00 1.056440e-03
## ea_tv_since_yng_br_3     0.0313621998 0.68457399 1.591642e+00 3.859352e-01
## ea_tv_since_yng_br_4     0.3241241405 3.59085005 2.233134e+00 1.849320e-01
## ea_tv_since_yng_br_5     3.2896705557 0.06083044 4.456949e-04 5.334042e+00
## ea_tv_since_yng_br_6     2.4562618291 2.16350296 6.983332e-02 4.976127e+00
## ea_boardg_since_yng_br_0 0.4229538359 1.45827301 3.212861e+00 1.770699e-02
## ea_boardg_since_yng_br_1 0.6773360718 0.50833361 3.833774e-01 1.087819e-01
## ea_boardg_since_yng_br_2 0.1644301885 0.74681947 1.447192e+00 9.851753e-03
## ea_boardg_since_yng_br_3 0.0964303206 1.32027062 1.182466e-01 2.576176e-01
## ea_boardg_since_yng_br_4 0.7313212568 2.56516372 1.731397e+00 2.305471e-02
## ea_boardg_since_yng_br_5 3.9544649885 0.58228948 1.512370e-01 4.935915e+00
## ea_boardg_since_yng_br_6 1.9981776284 1.48585147 3.999708e-01 4.980758e+00
## ea_vidg_since_yng_br_0   0.9402658381 1.92603506 2.778224e+00 7.555334e-02
## ea_vidg_since_yng_br_1   1.1274436028 0.87385316 5.276424e-01 4.026494e-01
## ea_vidg_since_yng_br_2   0.2994406887 0.22328845 2.142288e+00 2.962167e-03
## ea_vidg_since_yng_br_3   0.0331529287 0.60577887 1.000879e+00 2.067877e-01
## ea_vidg_since_yng_br_4   0.0896735221 3.60521927 2.260616e+00 2.179087e-01
## ea_vidg_since_yng_br_5   2.6319724844 0.09009768 2.661972e-03 4.193770e+00
## ea_vidg_since_yng_br_6   1.8945929378 1.70913144 2.889754e-02 4.384971e+00
## ea_edapp_since_yng_br_0  0.8567383880 1.31699941 2.368551e+00 6.930128e-02
## ea_edapp_since_yng_br_1  0.8301328317 0.59437845 7.668648e-01 2.416990e-01
## ea_edapp_since_yng_br_2  0.3471471895 0.08333706 1.732111e+00 1.282142e-04
## ea_edapp_since_yng_br_3  0.0023449258 0.60015515 1.102434e+00 2.617307e-01
## ea_edapp_since_yng_br_4  0.0659707787 4.13179722 1.214709e+00 1.389665e-01
## ea_edapp_since_yng_br_5  2.6532268511 0.04040317 1.284482e-02 3.837371e+00
## ea_edapp_since_yng_br_6  2.0407847145 1.35213443 5.901311e-02 3.894362e+00
##                                 Dim 5
## ea_read_b4_ALL_br_0      0.5505104647
## ea_read_b4_ALL_br_1      1.8510603347
## ea_read_b4_ALL_br_2      0.5802883185
## ea_read_b4_ALL_br_3      0.7188133451
## ea_read_b4_ALL_br_4      0.2266904807
## ea_read_b4_ALL_br_5      0.1036835869
## ea_read_b4_ALL_br_6      0.0151272235
## ea_indr_b4_ALL_br_0      1.2945951622
## ea_indr_b4_ALL_br_1      2.5565106083
## ea_indr_b4_ALL_br_2      1.1134375187
## ea_indr_b4_ALL_br_3      0.6204250247
## ea_indr_b4_ALL_br_4      1.0525486881
## ea_indr_b4_ALL_br_5      0.0071461088
## ea_indr_b4_ALL_br_6      0.0468949747
## ea_letter_b4_yng_br_0    0.9162802046
## ea_letter_b4_yng_br_1    3.1281721964
## ea_letter_b4_yng_br_2    1.9492034130
## ea_letter_b4_yng_br_3    0.3839976631
## ea_letter_b4_yng_br_4    1.3216816272
## ea_letter_b4_yng_br_5    0.0934111661
## ea_letter_b4_yng_br_6    0.0311942262
## ea_tv_b4_yng_br_0        1.5884434233
## ea_tv_b4_yng_br_1        3.1974930212
## ea_tv_b4_yng_br_2        1.2374859376
## ea_tv_b4_yng_br_3        1.1190440949
## ea_tv_b4_yng_br_4        1.3201442325
## ea_tv_b4_yng_br_5        0.0043950889
## ea_tv_b4_yng_br_6        0.1099806642
## ea_boardg_b4_yng_br_0    0.8502012967
## ea_boardg_b4_yng_br_1    1.6180300837
## ea_boardg_b4_yng_br_2    1.0022619635
## ea_boardg_b4_yng_br_3    0.0086928511
## ea_boardg_b4_yng_br_4    0.8053878836
## ea_boardg_b4_yng_br_5    0.0196664455
## ea_boardg_b4_yng_br_6    0.1007404374
## ea_vidg_b4_yng_br_0      1.8870945343
## ea_vidg_b4_yng_br_1      3.0189423311
## ea_vidg_b4_yng_br_2      1.2787852667
## ea_vidg_b4_yng_br_3      0.7805960082
## ea_vidg_b4_yng_br_4      1.5160963306
## ea_vidg_b4_yng_br_5      0.0257888016
## ea_vidg_b4_yng_br_6      0.1358086690
## ea_edapp_b4_yng_br_0     0.9561796105
## ea_edapp_b4_yng_br_1     2.3740009084
## ea_edapp_b4_yng_br_2     1.0848592223
## ea_edapp_b4_yng_br_3     0.7946416666
## ea_edapp_b4_yng_br_4     1.1483322677
## ea_edapp_b4_yng_br_5     0.0001304084
## ea_edapp_b4_yng_br_6     0.2918431706
## ea_read_since_yng_br_0   0.3350599668
## ea_read_since_yng_br_1   2.0734425800
## ea_read_since_yng_br_2   0.4368453755
## ea_read_since_yng_br_3   1.1562902179
## ea_read_since_yng_br_4   0.5346321614
## ea_read_since_yng_br_5   0.3909792624
## ea_read_since_yng_br_6   0.0069206436
## ea_indr_since_yng_br_0   1.5181009332
## ea_indr_since_yng_br_1   2.5656087574
## ea_indr_since_yng_br_2   0.6798235822
## ea_indr_since_yng_br_3   0.6811101495
## ea_indr_since_yng_br_4   0.8411390284
## ea_indr_since_yng_br_5   0.1376020932
## ea_indr_since_yng_br_6   0.0013607872
## ea_letter_since_yng_br_0 0.7109004629
## ea_letter_since_yng_br_1 3.1650517848
## ea_letter_since_yng_br_2 1.0264784456
## ea_letter_since_yng_br_3 1.8922779139
## ea_letter_since_yng_br_4 1.6968420377
## ea_letter_since_yng_br_5 0.2538366077
## ea_letter_since_yng_br_6 0.0645092422
## ea_tv_since_yng_br_0     1.6613082365
## ea_tv_since_yng_br_1     4.2648923150
## ea_tv_since_yng_br_2     0.5221400973
## ea_tv_since_yng_br_3     2.4992960782
## ea_tv_since_yng_br_4     0.9515442888
## ea_tv_since_yng_br_5     0.2501532204
## ea_tv_since_yng_br_6     0.1065625599
## ea_boardg_since_yng_br_0 0.7959775483
## ea_boardg_since_yng_br_1 2.8451236174
## ea_boardg_since_yng_br_2 1.1963726598
## ea_boardg_since_yng_br_3 0.4329527050
## ea_boardg_since_yng_br_4 0.7630306039
## ea_boardg_since_yng_br_5 0.3541479709
## ea_boardg_since_yng_br_6 0.1299685423
## ea_vidg_since_yng_br_0   1.6916375025
## ea_vidg_since_yng_br_1   3.5084365331
## ea_vidg_since_yng_br_2   0.4836087139
## ea_vidg_since_yng_br_3   2.1266346293
## ea_vidg_since_yng_br_4   0.8625523166
## ea_vidg_since_yng_br_5   0.2020826176
## ea_vidg_since_yng_br_6   0.1164746352
## ea_edapp_since_yng_br_0  0.9111101204
## ea_edapp_since_yng_br_1  3.1470205340
## ea_edapp_since_yng_br_2  0.3994525669
## ea_edapp_since_yng_br_3  1.8384770155
## ea_edapp_since_yng_br_4  0.6995272942
## ea_edapp_since_yng_br_5  0.0822916949
## ea_edapp_since_yng_br_6  0.1516723928

#Correlação entre variáveis e dimensões principais correlação entre as variáveis e as dimensões principais do MCA

Pode-se observar que para as duas principais dimensões, as variáveis são homogeneamente bem distribuídas e explicadas pelas duas componentes.

fviz_mca_var(res.mca_3f, choice = "mca.cor", 
            repel = TRUE, # Avoid text overlapping (slow)
            ggtheme = theme_minimal())

head(round(var_3f$coord, 2), 4)
##                     Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_b4_ALL_br_0 -0.34  0.61 -0.57  0.00  0.34
## ea_read_b4_ALL_br_1 -0.33  0.23  0.28 -0.02 -0.49
## ea_read_b4_ALL_br_2 -0.21 -0.46  0.40  0.08  0.36
## ea_read_b4_ALL_br_3 -0.08 -0.36  0.45 -0.04  0.57

para visualizar apenas categorias de variáveis: Agora visualizando apenas as categorias de variáveis, ou seja, qual opção o responsável assinalou no questionário, é possível notas uma distribuição homogênea em torno das duas componentes, com isso as alternativas são bem explicadas por elas.

fviz_mca_var(res.mca_3f, 
             repel = TRUE, # Avoid text overlapping (slow)
             ggtheme = theme_minimal())
## Warning: ggrepel: 67 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

head(var_3f$cos2, 10)
##                            Dim 1      Dim 2       Dim 3        Dim 4
## ea_read_b4_ALL_br_0 0.0260574124 0.08371529 0.072126880 4.041297e-07
## ea_read_b4_ALL_br_1 0.0448499379 0.02276840 0.033808810 1.897086e-04
## ea_read_b4_ALL_br_2 0.0093831879 0.04330983 0.033451113 1.408046e-03
## ea_read_b4_ALL_br_3 0.0005769839 0.01150409 0.018503578 1.435830e-04
## ea_read_b4_ALL_br_4 0.0172953678 0.10279870 0.072670271 5.132296e-04
## ea_read_b4_ALL_br_5 0.2508210231 0.01352265 0.001586833 3.790419e-02
## ea_read_b4_ALL_br_6 0.0481409827 0.03174351 0.004276419 6.506199e-02
## ea_indr_b4_ALL_br_0 0.0269281600 0.07024188 0.149325242 2.045890e-03
## ea_indr_b4_ALL_br_1 0.0820914457 0.02623711 0.084602895 1.100822e-02
## ea_indr_b4_ALL_br_2 0.0126089514 0.01593367 0.032863427 2.433952e-03
##                            Dim 5
## ea_read_b4_ALL_br_0 0.0255419626
## ea_read_b4_ALL_br_1 0.0993397923
## ea_read_b4_ALL_br_2 0.0265532967
## ea_read_b4_ALL_br_3 0.0296844789
## ea_read_b4_ALL_br_4 0.0105387223
## ea_read_b4_ALL_br_5 0.0041925662
## ea_read_b4_ALL_br_6 0.0005833305
## ea_indr_b4_ALL_br_0 0.0601850542
## ea_indr_b4_ALL_br_1 0.1317351186
## ea_indr_b4_ALL_br_2 0.0518666561

Se uma categoria de variável é bem representada por duas dimensões, a soma do cos2 é próxima de um. Para alguns dos itens de linha, são necessárias mais de 2 dimensões para representar perfeitamente os dados.

fviz_mca_var(res.mca_3f, col.var = "cos2",
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"), 
             repel = TRUE, # Avoid text overlapping
             ggtheme = theme_minimal())
## Warning: ggrepel: 68 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

fviz_cos2(res.mca_3f, choice = "var", axes = 1:2)

head(round(var_3f$contrib,2), 49)
##                       Dim 1 Dim 2 Dim 3 Dim 4 Dim 5
## ea_read_b4_ALL_br_0    0.31  1.22  1.29  0.00  0.55
## ea_read_b4_ALL_br_1    0.47  0.29  0.52  0.00  1.85
## ea_read_b4_ALL_br_2    0.11  0.64  0.61  0.03  0.58
## ea_read_b4_ALL_br_3    0.01  0.19  0.37  0.00  0.72
## ea_read_b4_ALL_br_4    0.21  1.49  1.29  0.01  0.23
## ea_read_b4_ALL_br_5    3.45  0.23  0.03  0.86  0.10
## ea_read_b4_ALL_br_6    0.69  0.56  0.09  1.55  0.02
## ea_indr_b4_ALL_br_0    0.32  1.02  2.66  0.04  1.29
## ea_indr_b4_ALL_br_1    0.89  0.34  1.36  0.20  2.56
## ea_indr_b4_ALL_br_2    0.15  0.23  0.58  0.05  1.11
## ea_indr_b4_ALL_br_3    0.03  0.70  0.21  0.03  0.62
## ea_indr_b4_ALL_br_4    0.59  1.36  1.50  0.01  1.05
## ea_indr_b4_ALL_br_5    2.62  0.26  0.17  0.11  0.01
## ea_indr_b4_ALL_br_6    0.76  0.47  0.07  0.53  0.05
## ea_letter_b4_yng_br_0  0.68  1.06  2.35  0.01  0.92
## ea_letter_b4_yng_br_1  0.57  0.31  1.18  0.06  3.13
## ea_letter_b4_yng_br_2  0.00  1.30  1.25  0.22  1.95
## ea_letter_b4_yng_br_3  0.07  1.07  0.02  0.00  0.38
## ea_letter_b4_yng_br_4  1.27  1.59  1.53  0.05  1.32
## ea_letter_b4_yng_br_5  5.11  1.16  0.25  1.45  0.09
## ea_letter_b4_yng_br_6  0.70  0.27  0.23  1.53  0.03
## ea_tv_b4_yng_br_0      0.97  1.55  2.67  0.11  1.59
## ea_tv_b4_yng_br_1      0.95  0.55  1.16  0.31  3.20
## ea_tv_b4_yng_br_2      0.23  0.63  1.52  0.09  1.24
## ea_tv_b4_yng_br_3      0.01  0.73  0.29  0.44  1.12
## ea_tv_b4_yng_br_4      0.64  2.52  1.93  0.09  1.32
## ea_tv_b4_yng_br_5      4.35  0.51  0.12  3.74  0.00
## ea_tv_b4_yng_br_6      1.12  1.06  0.00  5.48  0.11
## ea_boardg_b4_yng_br_0  0.31  1.92  2.05  0.05  0.85
## ea_boardg_b4_yng_br_1  0.60  0.09  0.51  0.32  1.62
## ea_boardg_b4_yng_br_2  0.03  0.99  1.04  0.03  1.00
## ea_boardg_b4_yng_br_3  0.33  1.38  0.01  0.04  0.01
## ea_boardg_b4_yng_br_4  0.37  1.77  1.12  0.12  0.81
## ea_boardg_b4_yng_br_5  4.15  0.73  0.09  2.10  0.02
## ea_boardg_b4_yng_br_6  1.61  0.59  0.19  2.90  0.10
## ea_vidg_b4_yng_br_0    0.71  1.22  2.47  0.14  1.89
## ea_vidg_b4_yng_br_1    1.06  0.60  1.39  0.15  3.02
## ea_vidg_b4_yng_br_2    0.21  0.77  1.63  0.02  1.28
## ea_vidg_b4_yng_br_3    0.12  0.66  0.07  0.30  0.78
## ea_vidg_b4_yng_br_4    0.32  2.19  2.74  0.02  1.52
## ea_vidg_b4_yng_br_5    3.96  0.22  0.12  3.98  0.03
## ea_vidg_b4_yng_br_6    1.37  1.58  0.00  7.42  0.14
## ea_edapp_b4_yng_br_0   0.45  0.97  1.94  0.08  0.96
## ea_edapp_b4_yng_br_1   1.03  0.43  0.87  0.20  2.37
## ea_edapp_b4_yng_br_2   0.14  0.43  2.08  0.00  1.08
## ea_edapp_b4_yng_br_3   0.02  0.52  0.18  0.49  0.79
## ea_edapp_b4_yng_br_4   0.36  3.06  2.17  0.00  1.15
## ea_edapp_b4_yng_br_5   3.02  0.21  0.01  3.10  0.00
## ea_edapp_b4_yng_br_6   1.87  1.55  0.06  5.21  0.29

15 principais categorias de variáveis que contribuem para as dimensões:

# Contributions of rows to dimension 1
fviz_contrib(res.mca_3f, choice = "var", axes = 1, top = 15)

# Contributions of rows to dimension 2
fviz_contrib(res.mca_3f, choice = "var", axes = 2, top = 15)

fviz_contrib(res.mca_3f, choice = "var", axes = 1:2, top = 15)

fviz_mca_var(res.mca_3f, alpha.var="contrib",
             repel = TRUE,
             ggtheme = theme_minimal())
## Warning: ggrepel: 68 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

##Gráfico de indivíduos Resultados

lista contendo as coordenadas, o cos2 e as contribuições dos indivíduos:

ind_3f <- get_mca_ind(res.mca_3f)
ind_3f
## Multiple Correspondence Analysis Results for individuals
##  ===================================================
##   Name       Description                       
## 1 "$coord"   "Coordinates for the individuals" 
## 2 "$cos2"    "Cos2 for the individuals"        
## 3 "$contrib" "contributions of the individuals"

colorir os indivíduos por seus valores de cos2:

fviz_mca_ind(res.mca_3f, col.ind = "cos2", 
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE, # Avoid text overlapping (slow if many points)
             ggtheme = theme_minimal())
## Warning: ggrepel: 525 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

# Cos2 of individuals
fviz_cos2(res.mca_3f, choice = "ind", axes = 1:2, top = 20)

# Contribution of individuals to the dimensions
fviz_contrib(res.mca_3f, choice = "ind", axes = 1:2, top = 20)

PCA COMO NUMERICO

PRE-PANDEMIA

Com o pacote “psych” Como o p-valor e praticamente 0, a matrix de correlacao nao e diagonal, ou seja, as variaveis seao correlacionadas.

require(psych)

cortest.bartlett(db_bloco_3_pre)
## $chisq
## [1] 1612.427
## 
## $p.value
## [1] 0
## 
## $df
## [1] 21

The test measures sampling adequacy for each variable in the model and for the complete model. The statistic is a measure of the proportion of variance among variables that might be common variance. The lower the proportion, the more suited your data is to Factor Analysis.

KMO(db_bloco_3_pre)
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = db_bloco_3_pre)
## Overall MSA =  0.82
## MSA for each item = 
##   ea_read_b4_ALL_br   ea_indr_b4_ALL_br ea_letter_b4_yng_br     ea_tv_b4_yng_br 
##                0.83                0.85                0.81                0.84 
## ea_boardg_b4_yng_br   ea_vidg_b4_yng_br  ea_edapp_b4_yng_br 
##                0.80                0.79                0.82
res.pca_3_pre <- PCA(db_bloco_3_pre)

res.pca_3_pre$eig
##        eigenvalue percentage of variance cumulative percentage of variance
## comp 1  3.4809424              49.727749                          49.72775
## comp 2  1.1398488              16.283554                          66.01130
## comp 3  0.7329582              10.470831                          76.48213
## comp 4  0.6073807               8.676867                          85.15900
## comp 5  0.3908762               5.583946                          90.74295
## comp 6  0.3638460               5.197800                          95.94075
## comp 7  0.2841477               4.059252                         100.00000
PCA_3_pre <- prcomp(db_bloco_3_pre, scale = TRUE)
print(PCA_3_pre)
## Standard deviations (1, .., p=7):
## [1] 1.8657284 1.0676370 0.8561298 0.7793463 0.6252010 0.6031965 0.5330550
## 
## Rotation (n x k) = (7 x 7):
##                           PC1        PC2         PC3         PC4         PC5
## ea_read_b4_ALL_br   0.2826357 -0.4830310 -0.64011136  0.47003914  0.20002989
## ea_indr_b4_ALL_br   0.3285644 -0.4135537 -0.18772773 -0.79496576 -0.05989485
## ea_letter_b4_yng_br 0.3967780 -0.2600564  0.40839105  0.35503281 -0.51659110
## ea_tv_b4_yng_br     0.4336358  0.2763527 -0.13630629 -0.02636210 -0.51375515
## ea_boardg_b4_yng_br 0.3836164 -0.2484833  0.58651398  0.01499558  0.53886532
## ea_vidg_b4_yng_br   0.4156935  0.4129075 -0.07472999 -0.10468235  0.12411495
## ea_edapp_b4_yng_br  0.3830498  0.4684204 -0.14166817  0.09573986  0.34613519
##                            PC6         PC7
## ea_read_b4_ALL_br   -0.1252620  0.02040298
## ea_indr_b4_ALL_br    0.2218518  0.03163696
## ea_letter_b4_yng_br  0.3650744  0.28628771
## ea_tv_b4_yng_br     -0.3982876 -0.54197550
## ea_boardg_b4_yng_br -0.2988610 -0.25918579
## ea_vidg_b4_yng_br   -0.3517244  0.70784800
## ea_edapp_b4_yng_br   0.6558589 -0.23379194
summary(PCA_3_pre)
## Importance of components:
##                           PC1    PC2    PC3     PC4     PC5     PC6     PC7
## Standard deviation     1.8657 1.0676 0.8561 0.77935 0.62520 0.60320 0.53306
## Proportion of Variance 0.4973 0.1628 0.1047 0.08677 0.05584 0.05198 0.04059
## Cumulative Proportion  0.4973 0.6601 0.7648 0.85159 0.90743 0.95941 1.00000
screeplot(PCA_3_pre, type = c("lines"), main = deparse(substitute(PCA)))

 biplot(princomp(db_bloco_3_pre, cor = T))

fviz_pca_ind(res.pca_3_pre,
             col.ind = "cos2", # Color by the quality of representation
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE     # Avoid text overlapping
             )
## Warning: ggrepel: 540 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

fviz_pca_var(res.pca_3_pre,
             col.var = "contrib", # Color by contributions to the PC
             gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
             repel = TRUE     # Avoid text overlapping
             )

fviz_pca_biplot(res.pca_3_pre, repel = TRUE,
                col.var = "#2E9FDF", # Variables color
                col.ind = "#696969"  # Individuals color
                )
## Warning: ggrepel: 528 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps